Current PhD vacancies

Biochemistry & Biophysics Cancer Immunology Wet lab

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Adoptive immunotherapy with engineered T cells expressing a Chimeric Antigen Receptor (CAR) has shown tremendous success rates in hematologic malignancies. However, the efficacy of CAR T cells in solid tumors is still limited, mainly due to insufficient trafficking and persistence of CAR T cells. Efforts in optimizing the efficiency of CARs were so far focusing on the extracellular antigen-binding domain or the overall CAR design. However, although it is known that downstream signaling in CARs is differing from the natural T cell receptor (TCR), the optimization of CAR signaling is often overlooked.
Signaling of the TCR occurs through binding of co-receptor associated or free lymphocyte associated kinase (Lck) and subsequently, binding of the kinase Zeta-chain-associated protein kinase of 70 kDa (ZAP70). In contrast, in CAR T cells, signaling through Lck is often bypassed and compensated by signaling through other downstream kinases.

In this project, directed evolution will be employed to engineer the signaling domains of CAR T cells. Using random mutagenesis and yeast surface display, methods from protein engineering will be employed to optimize the downstream signaling of CARs. The project will cover a broad range of methods, ranging from molecular biology, protein engineering to flow cytometry, T cell culture and genetic modification and immunological assays. As a PhD student on this project, you will not only acquire this skill set but will also contribute to a basic understanding of CAR T cell signaling and pave the way for next generation CAR T cells. 

AI/ML Bioinformatics Cancer Computer lab Medical physics & Biophysics

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We are  excited to announce two funded PhD positions for ambitious candidates eager to revolutionize the field of radiation oncology through advanced deep learning techniques. Modern radiation oncology is poised for a significant breakthrough, focusing on delivering personalized care by adapting treatments in real-time to patients' anatomical changes. While advancements in image-guided systems and delivery techniques have paved the way, the full potential of real-time adaptive strategies remains untapped due to limitations in automation and speed within current Treatment Planning Systems (TPS).

Our groundbreaking project aims to overcome these challenges by integrating cutting-edge Deep Learning (DL) and Reinforcement Learning (RL) technologies into the treatment planning process. We have three core objectives:

• Pioneer a Fully Autonomous Treatment Planning Pipeline: Develop the world's first radiation therapy planning system that operates independently of conventional TPS, leveraging advanced DL architectures.
• Explore Reinforcement Learning in Treatment Planning: Investigate how RL can enhance the planning process by directly optimizing machine parameters in Volumetric Modulated Arc Therapy (VMAT) plans in radiotherapy
• Establish a Next-Generation Workflow: Create a real-time adaptive treatment workflow that significantly improves the effectiveness and safety of radiation therapy.

As a PhD candidate in this project, you will become an integral part of a larger international research network focused on advancing automation in radiation oncology. Notably, Gerd Heilemann, a visiting researcher at Umeå University, is part of a complementary research project in this field with the Department of Diagnostics and Intervention, Umeå University. Additionally, the project will be connected to the recently initiated “Network for Equity in Automated Radiation oncology (NEAR)” with collaborating partners around the world.

Cardiovascular systems Human research Organ failure & replacement

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Are you passionate about transforming patient care through innovative research in cardiology? Join our dynamic research group at the Medical University of Vienna, a leading academic center dedicated to high-quality, advanced patient management. We are now accepting applications for a PhD position focused on exploring new frontiers in acute heart failure.

At out department you’ll be part of a collaborative and innovative research environment. You will work closely with our experienced faculty and have access to state-of-the-art facilities. Our team, consisting of clinicians, molecular biologists, and PhD students, is at the forefront of heart failure research. We work with extensive datasets both within our institution and as part of collaborative EU initiatives, which involve up to 1 million patient records. Our research focuses on addressing demanding clinical challenges in the field. We are also deeply involved in international collaborations throughout Europe, US and Asia, offering you the opportunity to engage in global research exchanges and contribute to impactful, real-world solutions. The results of our study group have contributed to current clinical guidelines recommendations. We design and conduct proof-of-concept pilot studies at the Medical University, which can then be followed by international multicenter trials. This specific PhD project is a pilot trial which will delve into the mechanisms of congestion in acute heart failure, monitor the decongestion process, and identify novel biomarkers including blood-based biomarkers, imaging and data from wearables. The ultimate goal is to optimize treatment strategies and improve patient outcomes.

 

Your Role

As a PhD student, you will play a significant role in conducting a monocentric clinical study, which includes patient recruitment, diagnostic procedures, database development, and comprehensive data analysis. In addition to your participation in this pivotal project, you will actively collaborate on other research initiatives within the group, contributing to a diverse range of innovative studies. You will develop and apply new analytical methods as well as advance your skills in data visualization and scientific writing. Your work will be essential in advancing both clinical insights and the academic output of our team. You will have the opportunity to present and discuss the results of your research at national and international conferences, as well as engage in scientific exchange with experts in the field.

Animal research Cancer Cardiovascular systems Endocrinology and Metabolism Organ failure & replacement

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Liver regeneration is a complex process that relies on the coordinated interaction of multiple cell types. Neutrophils are multifaceted players during infection, inflammation and tissue repair processes, but their role in liver regeneration remains incompletely understood. While neutrophil-derived growth factors exert beneficial roles in liver regeneration, we found neutrophil extracellular traps (NET)s associated with post hepatectomy liver failure (PHLF), a life-threatening complication with limited therapeutic options. By studying a mouse model of partial hepatectomy, we show that inhibiting NETs can enhance liver regeneration.

Hypotheses: Exploring the versatile role of neutrophils in liver regeneration and determining the optimal strategies to selectively target specific neutrophil functions may offer a novel approach to enhance patient outcomes following partial hepatectomy.

Approach: To achieve our objectives, we will employ state-of-the-art in vivo and in vitro assays to investigate the multifaceted role of neutrophils in liver regeneration. We will study neutrophil functions in animal models of partial hepatectomy with different treatment approaches and investigate cellular interactions that initiate regeneration processes via intravital microscopy and monitor regeneration processes over time. This will be complemented with patient data on different treatment regimen and in depth characterization of human liver tissue and longitudinal plasma analysis. These translational approach will provide valuable insights into the plasticity and functional importance of neutrophils in the regenerative process.

Level of originality: While neutrophils have been extensively studied in the context of liver inflammation and infection, their involvement in liver regeneration and failure is not yet fully understood. This research aims to uncover the specific mechanisms by which neutrophils influence liver regeneration and how their dysregulation can contribute to liver failure. This interdisciplinary approach enhances the originality and innovation of the research by bringing together diverse perspectives and expertises.

Throughout the project the PhD student will acquire expertise in flow cytometry, mouse models of liver inflammation and regeneration, and advanced imaging techniques. Additionally, the PhD position is part of a broader research network. As a participant in an NIH-funded program, the student will benefit from regular online meetings and collaborative exchanges with researchers from the University of Michigan and the University of Groningen. This network provides valuable opportunities for knowledge sharing, mentorship, and potential research collaborations with international experts in the field.

Animal research Immunology Molecular and cell biology Wet lab

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We are looking for motivated students interested in molecular immunobiology as well as murine immunization and genetic models. Our laboratories recently discovered that Rin-like (Rinl), a member of the Rin family of guanine nucleotide exchange factors, acts as negative regulator of T follicular helper (Tfh) cell differentiation (DOI: 10.1084/jem.20221466). T helper (Th) cells are crucial for adaptive immunity, as they orchestrate tailored immune responses to invading pathogens. Th subsets differentiate from naive CD4+ T cells, some giving support to B cells for high affinity antibody production such as Tfh cells and some migrating to the site of infection to coordinate immune responses. Th cell differentiation is tightly linked to the initial T cell activation via the TCR and CD28 co-stimulation. Therefore, regulators of T cell activation affect and shape Th differentiation. Current knowledge suggests that Rinl controls Tfh cell differentiation by affecting signaling pathways within CD4+ T cells. Based on these findings, we intend to decipher the Rinl-dependent molecular mechanisms that control Tfh differentiation by identifying and characterizing interaction partners of Rinl. To achieve a holistic view on the Rinl protein network we will apply proximity labeling combined with mass spectrometry. We will establish in vivo proximity labeling, characterize the in vivo network of Rinl protein interactions in CD4+ T cells at steady state and during an ongoing immune response. In addition, we will study Rinl interactors, their role in Rinl-dependent signaling events and in Th cell differentiation and fate. The project is a close collaboration between the Boucheron and Herbst labs. As selected candidate, you will benefit from exposure to a multidisciplinary and cooperative enviroment, cutting-edge research, scientific discussions and an international research network. As part of the Immunology PhD program, you will be in contact with groups working in Immunology all around Vienna and within the clinical departments from the general hospital in Vienna. You will also indirectly benefit from interactions with an SFB consortium, which has strong ties to proteomic and NGS facilities. Training opportunities include proximity labeling & proteomics studies, bioinformatics, immune cell biology, immunization and adoptive transfer models and primary T cell cultures.

Animal research Biochemistry & Biophysics Biomedical engineering Molecular and cell biology

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We are looking for motivated students interested in muscle cell biology as well as protein networks and cell signaling. Our lab is interested in the molecular mechanisms that govern the development of the neuromuscular junction (NMJ). The NMJ forms at the contact side between motor nerve and muscle fiber. NMJs receive chemical signals from the central nervous system to translate these into skeletal muscle contraction. Its proper development and function are crucial for survival. The muscle-specific kinase MuSK is required for all aspects of NMJ development including formation, maturation and maintenance. Lack of MuSK is lethal and defective MuSK signaling causes muscle weakness. Despite recent advances, signal transduction induced by MuSK activation is still largely unresolved. Consequently, understanding all the events of the signaling cascade(s) and their spatial-temporal organization remains a great knowledge gap. Therefore, we will perform a proximity labelling approach using muscle cells to decipher proteins within this network and their temporal interactions. Further, we will use in vitro and in vivo approaches including loss-of-function and gain-of-function in muscle cells as well as in muscle fibers to establish the role of the interactors during MuSK signaling and NMJ formation. Our rationale to combine proximity labeling with the analysis of spatio-temporal interactions will fill the existing gaps in the MuSK signaling cascade for a better understanding of NMJ development and function.

Biochemistry & Biophysics Drug targets & drug development Wet lab

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Current opioid-based analgesics are effective painkillers but with the drawback of severe side-effect of addiction and respiratory depression, which is the cause for the so-called ‚opioid epidemic‘. Here, the κ-opioid receptor is thought to be a promising alternative drug target to develop novel analgesics and anti-inflammatory agents without undesired effects. The project focuses on the molecular pharmacology and cellular signalling of stabilized and circular peptides at the κ-opioid receptor. We are looking for a PhD student with a degree in pharmacology, biochemistry, pharmacy or a related discipline. Join us at MedUni Vienna to explore the pharmacology of innovative peptides targeting the κ-opioid receptor in the periphery. The selected candidate will engage in an unique and innovative set-up focused on peptide drug discovery, peptidomics, receptor signaling, and preclinical expertise. The role involves designing and developing novel peptide-based drug candidates for G protein-coupled receptors. Training opportunities include peptide synthesis, stability assays, structural analysis of compounds alone and in complex with the receptor, AI/ML-assisted signaling studies, detailed in vitro receptor pharmacology, peptide biodistribution, and in vivo experiments in relevant animal models.

The selected candidate will engage in an unique and innovative set-up focused on peptide drug discovery, peptidomics, receptor signaling, and preclinical expertise. The role involves designing and developing novel peptide-based drug candidates for G protein-coupled receptors. Training opportunities include peptide synthesis, stability assays, structural analysis of compounds alone and in complex with the receptor, AI/ML-assisted signaling studies, detailed in vitro receptor pharmacology, peptide biodistribution, and in vivo experiments in relevant animal models. As a member of an international and dynamic research group, you will benefit from exposure to cutting-edge research published in top-tier, peer-reviewed journals.

We have established collaborations with leading institutions in Australia (Institute for Molecular Bioscience and the South Australian Health and Medical Research Institute) and the USA (Center for Clinical Pharmacology, Washington University in St. Louis). Candidates will also have the opportunity for short-term research placements in these collaborating labs.

AI/ML Bioinformatics Drug targets & drug development Mathematics & statistics Medical physics & Biophysics Molecular and cell biology Neuroscience

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The Transient Receptor Potential (TRP) channels belongs to a larger family of ion channels, which are responsible for detecting various stimuli in the body. TRP channels are regulated by lipids, acting as gates for Ca2+, which intracellularly serve as signalling molecules to regulate cellular function. The main focus of this project is on TRPC3 and TPRV6, which among other activities are involved in calcium absorption in the intestine and in the maintenance calcium homeostasis. Ca2+ plays an vital role in many cellular processes, including muscle contraction, nerve cell signalling, and cell growth.

This project focuses on the precise channel regulation and on signal transduction. The current knowledge on TRP channel regulation is at its infancy, while the diseases associated with disfunction mandate its investigation to create the knowledge base for helping patients in the future. Regulatory lipids bind at the periphery, but the mechanism of allosteric communication to the ion permeation path is unknown.

The aim of this study is to understand channel regulation and channel function at the atomistic level. Crucial is to uncover the molecular details of ligand binding and of allosteric signalling communction of regulatory lipid binding through the channel structure to the ion permeation pore to understand regulation. You will extensively be using computational approaches, including bioinformatics, AI, docking, MD simulations and free energy calculations, and characterize dynamics, forces and free energy profiles. Your computation work will be carried our hand in hand with experimental collaboration partners working on the same question.

 

Biochemistry & Biophysics Cancer Endocrinology and Metabolism Wet lab

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Do you want to do lead a game-changing scientific discovery? Ready to tackle a long-standing scientific question? Are you curious about novel targets that could halt the metabolism of cancer? If so, we invite you to apply to the Oncometabolism Laboratory at the Center for Cancer Research. Since Sir Hans Krebs discovered the mitochondrial tricarboxylic acid cycle (i.e. Krebs cycle) it has been well-established that glutamine contributes its carbons to the cycle, supporting mitochondrial respiration and anabolic intermediate production. However, the precise mechanism by which glutamine enters the mitochondrial matrix remains largely unknown, and the glutamine-specific mitochondrial carriers remained elusive until now. In this project we have conducted a CRISPR- and mass spectrometry-based metabolomic screen to identify potential mitochondrial glutamine carriers. Several promising candidates have emerged from our data, and during your PhD you will take the lead in identifying, validating, and characterizing these candidates. Additionally, you will explore the functional impact of deleting individual solute carriers (SLC) in cancer cells. By combining cutting-edge technology with your enthusiasm, skills, and dedication, you could be at the forefront of discoveries with wide-reaching implications for physiology and cancer treatment. Join us and make a difference.

AI/ML Computer lab Mathematics & statistics Neuroscience

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Do you want to push the boundaries of brain-machine interfaces and neuroprosthetics? Are you excited about combining machine learning with neural and kinematic data to unlock new possibilities in motor control and robotics? Join our ambitious project to create AI-based controllers for advanced neuroprosthetic solutions that restore fluid, natural movement in individuals with paralysis. Recent advances in brain-machine interfaces have enabled remarkable achievements, from decoding speech in stroke patients to restoring walking in paralysis patients. Yet, neuroprosthetics for walking have relied on repurposing high-level motor cortex activity, designed for complex goal-directed actions, to drive simple, deliberate leg movements. To achieve fluid, natural locomotion, we need to go beyond this and develop a deeper understanding of how cortical neural signals translate into complex, dynamic movements.

Your Role

As a PhD candidate, you will work on a one-of-a-kind vast dataset of primate locomotion, combining high-density neural recordings from three brain regions with detailed 3D motion tracking data. Your goal is to develop a generative foundation model that simulates primate movement, controlled directly by neural recordings. This embodied simulation will provide a powerful tool to study motor control and improve the design of neuroprosthetics. In the process, you will develop an in-depth expertise to work with neural and kinematic data using advanced signal processing techniques. You will also develop novel ML algorithms and controllers that allow the embedded simulation to generalise across environments. You will be able to significantly further our understanding of motor control and have the possibility to translate our findings into the medical domain.

Why You Should Apply

• High-Impact Research: Contribute to a project that has the potential to transform the field of neuroprosthetics, improving mobility for individuals with paralysis.
• Cutting-Edge Machine learning: Work with unique datasets that combine neural recordings and motion capture, developing foundation models for embodied simulations.
• Interdisciplinary: Combine neuroscience, robotics, machine learning, and reinforcement learning to solve complex motor control challenges.
• Global Collaborations: Collaborate with top institutions in machine learning, robotics, and neuroengineering, including EPFL, MIT, and Imperial College London.
• Career Growth: Your work will position you at the forefront of both neuroscience and robotics, opening doors for publications, conference presentations, and future research opportunities.

AI/ML Computer lab Mathematics & statistics Neuroscience

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Are you passionate about developing cutting-edge machine learning models to tackle one of the most fundamental challenges in neuroscience? Join us on an exciting journey to understand how the coordinated activity of neurons across multiple brain regions leads to robust, adaptive behavior. Thanks to groundbreaking single-cell recording technologies like Neuropixels and Ca+ imaging, we can now record neural activity from large populations of neurons. Yet, these tools are often constrained to local brain regions, either at superficial or deep layers. Our project seeks to leverage vast datasets from the International Brain Laboratory, Allen Institute for Visual Coding, and other collaborators to tackle this limitation. These datasets offer an unparalleled opportunity to link activity at the single-neuron level to whole-brain states, potentially transforming how we understand brain pathologies and bridging the gap between clinical imaging (e.g., fMRI) and laboratory-based cellular recordings.

Your Role

As a PhD candidate, you will lead the development of novel machine learning algorithms and advanced signal processing techniques to unify isolated neural recordings into a predictive model that spans the entire mammalian brain. You will be at the forefront of a new era in neuroscience, helping to quantitatively model single-neuron dynamics on a large scale—a feat previously limited to low-resolution techniques like fMRI. Beyond neuroscience, your work will contribute to advances in machine learning theory, especially in the development of large-scale models that capture complex neural dynamics during cognition.

Why You Should Apply

• High Impact Research: The results of this project have the potential to challenge and redefine the "one region, one computation" perspective, offering a more integrated understanding of distributed brain functions.
• Cutting-Edge Technology: Work with advanced datasets and technologies, from single-neuron recordings to whole-brain imaging.
• Career Growth: This PhD will position you at the intersection of neuroscience and machine learning, with opportunities to publish in top journals and present at leading international conferences. 
• Global Collaborations: Collaborate with renowned institutions, including EPFL, MIT, and Imperial College London, gaining exposure to a vibrant international research network.

AI/ML Data Science Bioinformatics Biomedical engineering Cancer Computer lab Drug targets & drug development Immunology Molecular and cell biology Neuroscience

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Integrative analysis of single-cell multi-omics and spatial imaging data in cancer, immunology, organoids, etc., in the context of the Human Cell Atlas (single-cell analytics) and/or the ELLIS European Lab for Learning & Intelligent Systems (ML/AI). Development and application of generative AI methods for large-scale CRISPR perturbation screening and cell engineering as personalized therapies.

AI/ML Computer lab Mathematics & statistics

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We are a new research group at the Institute of Artificial Intelligence at the Medical University of Vienna, located in the heart of Vienna. We develop machine learning models that leverage existing knowledge in molecular biology to derive more tangible insights from large biomedical datasets. Methodologically, we achieve this by equipping machine learning and AI models with access to extensive biological knowledge, utilizing large-scale priors and inductive priors structured in networks or graphs — recently, for example, through graph neural networks. This position focuses on modeling dynamic systems, specifically inferring homeostatic and dynamic mechanisms from snapshot experiments common in cell biology, such as single-cell and spatial transcriptomics. We aim to build “virtual cells”: computational methods that allow us to simulate how cells in organisms change during disease and treatment. We are particularly excited about applying these machine learning methods to immuno-oncology, infectious disease, autoimmunity, and the engineering of cellular therapeutics.

During your PhD, you will contribute creatively and proactively to the development of new mechanistic machine learning methods tailored to cutting-edge omics experiments and their application in biomedicine as part of interdisciplinary teams. Beyond science, we care about your professional development and are committed to helping you define and achieve goals that align with your career trajectory. You will learn to design, implement, test, and deploy machine learning models for biomedical data, with a particular focus on single-cell and spatial omics. You will work in collaboration with experimental groups and work with machine learning models in the context of specific datasets and questions in these collaborations. You will learn how to use existing knowledge from molecular biology to improve machine learning models of cellular function. In this context, you will gain a deep understanding of relevant machine learning models and mathematical modeling ideas that can use this knowledge.

AI/ML Data Science Bioinformatics Computer lab

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The AI Institute / Center for Medical Data Science is recruiting an ambitious PhD student who wants to pursue a scientific career in the domain of Machine/Deep Learning for Medical Imaging with applications in eye care. The position is on automated detection and profiling of retinal disease as part of EU project I-SCREEN (https://www.i-screen.eu/). The output will have a real-world impact on healthcare by enabling community-based screening of patients suffering from retinal diseases, a leading cause of blindness today.

The focus of the research is on building robust, reliable, and interpretable models to predict the patient-specific progression of retinal disease from 3D optical coherence tomography (OCT) scans of the human eye to enable generalizable and trustworthy AI-based prognostic tools. Research topics will span the areas of Deep Learning for Survival Analysis, Self-Supervised Learning, and Domain Adaptation. The candidate will be immersed in a multidisciplinary environment working closely with a team of computer scientists, software engineers, and medical doctors in the fascinating and interdisciplinary field of AI in Healthcare.

We are embedded in a vibrant network of a rapidly growing cluster of AI researchers dedicated to addressing the challenges of AI in medicine, and benefit from the methodological expertise in machine/deep learning at the AI Institute. Throughout their PhD in our group, the student will acquire in-depth knowledge and hands-on experience in advanced deep learning methodologies and statistical modelling. They will develop the skills to apply these techniques to complex and high-dimensional medical imaging datasets, gaining expertise in designing, implementing, and optimizing machine learning models for healthcare.

We offer

• Opportunity to work and do cutting-edge research in deep learning for medicine and healthcare.

• Immersion into an interdisciplinary and international research environment, and a multi-cultural lab.

• Access to extremely large multi-modal, curated, and annotated medical imaging datasets.

• Access to a dedicated high-performance computing (HPC) cluster containing the latest generation GPUs.

• Collaboration with several renowned academic institutions, as well as partnership with imaging device companies.