Our lab integrates structural and functional studies, protein engineering, and cell/molecular biology to address fundamental and translational challenges in neuroscience. By combining expertise across molecular biology, protein and chemical engineering, and neurobiology, we strive to develop cutting-edge solutions for pressing neurobiological questions, including optogenetics and cancer neuroscience.

We are seeking passionate and driven researchers to contribute to interdisciplinary projects spanning multiple areas (researchers are encouraged to work across disciplines rather than being confined to a single focus):

• Protein Biochemistry: Investigating and characterizing key proteins that play critical roles in neurobiology, particularly in optogenetics and brain cancer research.
• Protein Engineering: Leveraging molecular and chemical engineering strategies to explore and develop novel therapeutic approaches for neurological diseases, with a focus on pediatric brain cancers.
• Technology Development: Innovating computational and experimental platforms to advance protein engineering applications in neuroscience.

Contact email: kys8892@gmail.com

LBNC

We are looking to hire a graduate student in cell-free synthetic biology. The prospective graduate student will work on building the foundations for the development of a synthetic cell. This project will involve developing state-of-art techniques and approaches in cell-free synthetic biology combined with microfluidic technologies to push the current boundaries of in vitro synthetic biology and cell-free transcription – translation systems. The graduate student will be embedded in a highly international and dynamic research environment.

Oates lab

Extrinsic control of intrinsic cellular timing

How do spatio-temporal patterns emerge at the tissue level from noisy cellular and molecular interactions? What are the principles that govern transitions from parts to wholes, and those that determine precision and robustness? We explore these issues using a population of genetic oscillators in the vertebrate embryo termed the segmentation clock that governs the rhythmic and sequential subdivision of the body into somites that later build the segmented bones and muscles of the adult body.

We have an opening in the group for a curious, indepedent and motivated person who would like to explore the balance of cell-intrinsic timing and extrinsic signaling factors in the segmentation clock. This person will build on our recent work in Rohde et al., eLife     https://doi.org/10.7554/eLife.93764.2.

Questions adressed by the project include: What temporal behaviors are encoded within cells of the segmentation clock? What is the molecular basis of these cell-intrinsic programs? Which signals from other cells or cues from the embryonic environment influence the intrinsic behavior? What dynamics in these noisy signals are relevant for information transfer, and how are they decoded by indivdual or groups of cells?

We use the zebrafish as a model system, combining genetic engineering, time-lapse microscopy of single cells and embryos, image processing, various omics approaches, biochemistry, data analysis and modeling to understand the biology. We are looking for people with a mix of experimental and computational skills. If this sounds interesting to you, then please apply to the Timing, Oscillators, Patterns lab.

Unravelling the impact of different parameters on the development of osteoarthritis

Background

The multifactorial nature of osteoarthritis necessitates not only identifying associations but also establishing causal relationships among contributing factors such as biomechanical stress, inflammation, aging, and metabolic dysfunction. To address this complexity, each specific effect will be examined through scientific models represented as Directed Acyclic Graphs (DAGs), which clarify underlying assumptions and causal structures.

Using do-calculus, we will derive the minimal adjustment sets necessary for isolating causal effects in each scenario. This approach ensures that confounding is appropriately addressed, enhancing the validity of the inferences. Effects will then be estimated within a Bayesian framework, allowing for the incorporation of prior knowledge and explicit modeling of uncertainty. Models will be implemented in Stan and analyzed in R, enabling flexible, transparent, and reproducible statistical workflows.

By integrating causal modeling with Bayesian estimation, this methodology provides a robust foundation for identifying key drivers in osteoarthritis development and progression. It opens the door to more targeted interventions, improved understanding of disease mechanisms, and ultimately, advances in precision medicine for osteoarthritis care.

PhD Candidate Profile: Background and Expertise

The ideal PhD candidate for this project will have a strong interdisciplinary background combining quantitative sciences, biomedical research, and computational modeling. Given the project’s emphasis on causal inference, statistical modeling, and osteoarthritis mechanisms, the candidate should bring both technical and biological understanding to the table.

Academic Background:

• Master’s degree (or equivalent) in one of the following fields:
• Biostatistics, Computational Biology, Applied Mathematics
• Biomedical Engineering, Bioinformatics, or Epidemiology
• Alternatively, a background in Data Science or Physics with a demonstrated interest in life sciences

Key Competencies:

• Strong foundation in statistics and probability, particularly Bayesian methods
• Experience with causal inference frameworks or a willingness to acquire expertise in do-calculus, DAGs, and structural causal models
• Proficiency in R and familiarity with Stan or other probabilistic programming languages
• Experience or interest in modeling biological systems, especially in musculoskeletal or joint biology, is an asset
• Familiarity with machine learning, data visualization, and multi-omics data analysis is a plus

Personal Attributes:

• Curiosity-driven and eager to work at the interface of data science and biomedical research
• Strong analytical thinking, problem-solving skills, and scientific rigor
• Excellent communication skills and ability to work collaboratively in a multidisciplinary environment
• Comfortable engaging with both theoretical modeling and biomedical applications

Causal Bayesian Networks to unravel the effect of different parameters on the initiation and development of osteoarthritis

Background

Osteoarthritis (OA) is the most common degenerative joint disease, affecting millions globally and causing pain, stiffness, and reduced mobility. Once considered a simple wear-and-tear condition, OA is now recognized as a complex disorder driven by mechanical, biological, and inflammatory factors. Understanding its multifactorial nature requires moving beyond associations to establish causal relationships among contributors such as biomechanical stress, inflammation, aging, and metabolic dysfunction.

The proposed study aims to identify and quantify these causal effects by integrating causal inference frameworks—including Directed Acyclic Graphs (DAGs), do-calculus, and Bayesian modeling—with data from clinical, biomechanical, and molecular sources. Data from different sources, such as the longitudinal OsteoLaus cohort, will be used. The goal is to uncover underlying disease onset and progression mechanisms, support the development of personalized, predictive models, and inform targeted prevention and treatment strategies in OA care.

PhD Candidate Profile: Background and Expertise

The ideal PhD candidate will possess a robust background in data science, biomedical research, and computational modeling, along with an interest in both quantitative methods and biological applications. A Master’s degree in biostatistics, biomedical engineering, computational biology, or a related field is required.

Key skills include:

• Proficiency in statistics, especially Bayesian methods
• Experience or interest in causal inference
• Interest in biological systems modeling, especially related to osteoarthritis

The candidate should be curious, analytical, collaborative, and comfortable working at the interface of data and biomedical sciences.

Using optogenetics, directed evolution, and AI to create new proteins

The Rahi lab has recently created a method for the directed evolution of switchable multi-state proteins. We are looking for a brilliant new PhD student with excellent

– experimental,

– problem-solving, and

– conceptualization skills

and a strong interest in computation to develop the next generation of directed evolution methods, in combination with optogenetics and AI.

Applications: controllable antibodies, optogenetic systems, orthogonal signaling systems.

For questions, email: sahand.rahi@epfl.ch

The Persat lab focuses on understanding how bacteria sense and respond to mechanical forces in their environments, particularly during infections. The team develops tissue-engineered organoids to study pathogens like Pseudomonas aeruginosa in realistic settings, aiming to elucidate how mechanical stimuli influence bacterial physiology and biofilm formation. By investigating bacterial mechanosensing mechanisms, such as the role of type IV pili in surface navigation and virulence regulation, the lab seeks to translate these insights into alternative therapeutic strategies to combat antibiotic-resistant infections.

The lab has two open PhD positions for projects that combine bioengineering of organoids, microfluidics, microscopy and computation with applications to antibiotic discovery.

Ramdya Neuroengineering Laboratory of Neuroengineering reverse-engineers cognitive and motor behaviors in the fly, Drosophila melanogaster, to better understand the mind and to design more intelligent robots. Flies are an ideal model: they generate complex behaviors, their nervous systems are small, and they are genetically malleable. Our lab develops and leverages advanced microscopy, machine learning, genetics, and computational modeling approaches to address systems-level questions. We are always looking for talented researchers to join our team. Join us! There is much to discover!

Immunoengineering project

Tang lab’s research aims at developing novel strategies to engineer immunity-disease interactions, an emerging field called ‘immunoengineering’, through chemical, metabolic, and mechanical means in order to treat cancer safely and effectively with immunotherapies. We are actively looking to recruit PhD students who are interested in this new field and would like to work in a highly interdisciplinary environment.

Focus of the Zenk lab:

The human body develops from a single totipotent cell. During development, this single totipotent cell gives rise to the entire diversity of cell types of the body that ultimately make up all organs. Even though those cells are transcriptionally and functionally different, they share the same genome. Epigenetic mechanisms that regulate which set of genes will be turned on and which genes will be switched off in each cell are at work in order to maintain and generate cellular diversity.

The nervous system develops during early embryonic development and ultimately contains all different types of neurons from different regions of the body. In a series of developmental transitions, progenitors differentiate into neuron and glia lineages.

In my lab, we use neural organoids to model these developmental transitions and investigate how epigenetic processes control differentiation and cell fate. We employ single-cell genomics and imaging technologies to profile the chromatin of individual cells.

We have multiple open positions.

The successful candidates should have:

-High motivation, curiosity and a strong interest in scientific discoveries

-Drive to learn innovative technologies and perform challenging experiments

-A strong background in computational analysis of genomics data

-Good experimental skills in molecular biology (e.g. IF, IP, Western-Blot, Nuclei-Acid-Extraction, Sequencing-library preparation, Cloning)

-Ideally, experience with human iPS cell culture and curiosity to further develop in vitro culture systems

We are two collaborative research groups at the intersection of synthetic biology, chemical biology, and protein engineering, jointly seeking highly motivated researchers to tackle fundamental and translational questions in immunology, cancer biology, and neurological disease.

Our laboratories develop innovative molecular tools to study membrane receptors, protein-protein interactions, and protein evolution, with the goal of advancing next-generation precision therapeutics and expanding our understanding of complex signaling networks in both healthy and diseased tissues.

Research Opportunities

Researchers joining our teams will have the opportunity to contribute to one or more of the following interdisciplinary areas:

• Synthetic Protein Interactions & Coevolution
  Design and implement high-throughput synthetic platforms to investigate protein-protein interactions involved in immune recognition, antigen presentation, checkpoint signaling, and immune evasion, combining experimental and computational approaches.
• Post-Translational Modification Biology
  Develop novel chemical biology and synthetic strategies to study PTMs and their roles in immune signaling, host-pathogen interactions, and tumor progression, with applications in both discovery biology and biomarker development.
• Therapeutic Innovation & Molecular Engineering
  Engineer immune cells, neural circuits, and molecular diagnostics using synthetic biology and AI-driven protein design, with translational goals in cancer immunotherapy, brain cancer treatment, and neurological disease intervention.

What We Offer

• A highly collaborative and interdisciplinary research environment
• Access to state-of-the-art technologies in protein engineering, structural biology, and synthetic biology/neurobiology
• Opportunities to pursue high-impact science at the interface of basic discovery and therapeutic development
• Mentorship tailored for future careers in academia, biotech, and translational research

Who Should Apply

We welcome applicants from diverse scientific backgrounds, including molecular biology, synthetic biology, biophysics, structural biology, immunology, neuroscience, and computational biology. Experience with protein design, molecular engineering, or cell-based functional assays is a plus but not required.

Contact emails: (aryang8825@gmail.com, kys8892@gmail.com)