Routine laboratory measurements, infrared molecular fingerprints, proteomic profiles, and metabolomic profiles provide complementary views of human physiology. Correlations across these modalities can reveal coordinated biological variation, but correlation alone cannot establish whether one molecular process influences another, whether the relationship is indirect, or whether both signals arise from a shared cause.

Longitudinal multimodal data provide a promising setting in which to investigate directional dependencies and construct more mechanistic models of physiological regulation, healthy aging, and changes in health status.

The project will develop and evaluate computational strategies for causal analysis across molecular modalities. Possible objectives include:

The work may address both individual molecular variables and lower-dimensional representations learned from high-dimensional proteomic, metabolomic, or spectroscopic measurements.

Depending on the student’s background, the project may involve constraint-based and score-based causal discovery, structural causal models, probabilistic graphical models, dynamic Bayesian networks, mediation analysis, multivariate statistics, network science, multimodal representation learning, and invariant or longitudinal prediction methods.

A major component will be evaluating the stability and credibility of inferred relationships. This may include bootstrap analysis, sensitivity to preprocessing and batch correction, comparison across visits or participant subgroups, negative controls, simulation studies, and explicit assessment of unmeasured confounding and identifiability. Where direction cannot be established from the available data, results will be reported as equivalence classes or competing causal explanations rather than overstated as definitive causation.

Applicants should be enrolled in a Master’s program in physics, biophysics, quantitative biology, mathematics, statistics, computer science, or a closely related quantitative field. Strong analytical and programming skills are expected, preferably in Python or R. Familiarity with statistics, machine learning, graphical models, network analysis, or omics data is beneficial. Curiosity about biological mechanisms and a willingness to engage critically with causal assumptions are especially important.

The student will gain experience in causal discovery, graphical and structural causal modeling, multimodal omics integration, longitudinal analysis, network interpretation, robustness assessment, reproducible research, and translating computational findings into biologically testable hypotheses.

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Observational data can reveal associations between molecular measurements and health states, but many scientifically important questions are counterfactual: How might the molecular phenotype of a particular individual have evolved under different conditions? What changes would be expected following a hypothetical intervention? Which molecular signals might precede or accompany a transition from health toward disease?

Such questions are challenging because the same individual cannot be observed simultaneously under factual and alternative conditions. Causal generative models offer a computational framework for constructing these individualized scenarios while making the underlying assumptions explicit.

The project will design, implement, and train models capable of generating plausible counterfactual molecular states or trajectories. Possible objectives include:

The analysis may combine routine laboratory measurements, infrared molecular fingerprints, proteomics, metabolomics, demographic variables, and longitudinal information.

Candidate approaches include conditional diffusion models, causal variational autoencoders, normalizing flows, structural causal models, representation learning, and probabilistic deep learning. Evaluation will go beyond visual or distributional realism and may assess composition, reversibility, intervention effectiveness, minimality, individual consistency, temporal plausibility, and sensitivity to alternative causal assumptions.

Because counterfactual conclusions depend on assumptions that may not be identifiable from observational data alone, careful model criticism and uncertainty analysis will be central to the project. Generated scenarios will be treated as research hypotheses rather than clinical predictions.

Applicants should be enrolled in a Master’s program in physics, biophysics, quantitative biology, mathematics, statistics, computer science, or a related quantitative discipline. A solid background in machine learning, probability, linear algebra, and programming is expected. Experience with Python and a deep-learning framework such as PyTorch or JAX is desirable. Prior knowledge of causal inference or generative modeling is advantageous but not essential.

The student will develop expertise in causal inference, modern generative AI, multimodal representation learning, scientific machine learning, model evaluation, GPU-based computation, and responsible interpretation of counterfactual models in biomedicine.

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Clinical measurements are commonly interpreted using reference intervals derived from populations. Such intervals may overlook biologically meaningful changes within an individual: a measurement can remain within the population range while deviating substantially from that person’s usual physiological state. Longitudinal molecular profiling creates an opportunity to define individualized baselines and detect departures from personal physiological normality.

The project will investigate how personalized reference intervals can be estimated from repeated measurements and extended from individual clinical variables to high-dimensional molecular profiles. Possible objectives include:

Particular attention may be given to calibration, uncertainty quantification, missing data, batch effects, temporal drift, and the number of observations required to construct a reliable personal baseline.

Depending on the student’s interests, the work may involve hierarchical or mixed-effects models, Bayesian inference, state-space models, Gaussian processes, time-series analysis, change-point and anomaly detection, representation learning, or multimodal latent-variable models. Methods will be evaluated through held-out longitudinal measurements, simulation studies, coverage and calibration analyses, and robustness tests.

Applicants should be enrolled in a Master’s program in physics, biophysics, quantitative biology, mathematics, statistics, computer science, or a closely related quantitative field. A strong foundation in statistics, probability, data analysis, or machine learning is expected. Experience with Python or R is highly desirable. Previous work with biomedical or omics data is helpful but not required.

The student will gain experience in longitudinal biomedical data analysis, personalized modeling, multimodal machine learning, uncertainty quantification, reproducible computational research, and the critical interpretation of statistical models in a health-monitoring context.

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Our research combines advanced InfraRed spectroscopy technologies with multi-omics, clinical diagnostics, and computational data analyses to develop novel molecular fingerprinting methods for human health assessment and early disease detection. The research builds on several clinical studies for which thousands of individuals contribute blood samples, resulting in rapidly growing datasets with rich molecular measurements and detailed clinical characterization. We are an interdisciplinary team with expertise spanning physics, data science, molecular science, and clinical research. Our main question is to understand how subtle molecular changes in blood relate to health states, disease onset, and longitudinal health trajectories, with applications in common diseases and cancer. To integrate our findings and generate new predictions and hypotheses, we use machine learning and systems analysis frameworks. Further information is available at www.attoworld.de/bird

Research area: Projects will focus on analysis of large-scale molecular datasets applying machine learning and data analysis. Research directions include the development of robust approaches for data standardization, improving model generalization across heterogeneous datasets, and extracting clinically meaningful insights from complex molecular data.

Prior experience in molecular biology, biophysics, or chemistry is advantageous but not essential. More important is the motivation to work across disciplines and to engage with biologically and clinically relevant questions. We value diversity and strongly encourage applications from all qualified individuals.

Applications, including a letter of motivation, a CV and the names and contact details of two referees should be sent directly to Prof. Mihaela Zigman and Dr. Tarek Eissa (mihaela.zigman@mpq.mpg.de; tarek.eissa@mpq.mpg.de).

The group is based at Garching Forschungszentrum. For PhD applicants, formal supervision can be arranged through LMU Physics or TUM School of Computation Information and Technology (CIT).

Download: Job Description (PDF)

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The professional will work in a highly international and interdisciplinary team in direct collaboration with laser scientists and engineers from CMF and LMU at the Center for Molecular Fingerprinting in Budapest, Hungary. We are seeking an experienced software developer/architect to join our team to support the automation of measurements and the reliable, high-throughput operation of ISA 3.1. The ideal candidate will design, implement, and maintain scalable software solutions that optimize resources, ensure data integrity, and minimize manual intervention.

• Master’s degree in Computer Science, Electrical Engineering, Physics, or a related field.
• Professional knowledge of software development principles: data structures, algorithms, complexity, system design, testing.
• Extensive experience in at least one text-based programming language (Python or C++ preferred).
• Experience with SQL databases (ideally PostgreSQL).
• Experience with version control systems (e.g., Git).
• Experience with or willingness to learn CI/CD pipelines and containerization.
• Ability to work cross-functionally in a fast-paced R&D environment.

• Strong experience in Labview and Python.
• Experience in developing high-throughput or automated measurement systems.
• Knowledge of software architecture for large-scale laboratory instruments.
• Agile, experimental, and iterative approach to software development.
• Strong problem-solving, debugging, and optimization skills.
• Familiarity with data security and sharing protocols.

• Work in state-of-the-art research laboratories.
• Help develop the next generation laser spectrometer for human health monitoring.
• Interdisciplinary research group with international atmosphere.
• Close collaboration with Research Center Garching, Faculty of Physics at LMU, Am Coulombwall 1, 85748 Garching
• Regular home office option
• Competitive salary and extensive benefits package

If you are interested in this exciting opportunity to contribute to advanced measurement automation, please send us your CV and study transcripts. Additionally, please include a short statement about your motivation.

Dr. Alexander Weigel
Email: alexander.weigel@mpq.mpg.de
Csaba Liber
Email: csaba.liber@cmf.hu

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If you’re eager to push the boundaries of infrared molecular fingerprinting and contribute to innovative biomedical diagnostics, we are looking forward to hear from you! Students from LMU, TUM or other universities are also welcome, provided formal supervision at their home university is ensured.

Learn more about our work:
www.attoworld.de/bird

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We are a dynamic, interdisciplinary team of physicists, data scientists, biologists, and clinical experts, all working together to harness infrared spectroscopy for medical applications. By detecting subtle changes in the molecular composition of blood, we aim to capture and characterize early markers of health and disease (www.attoworld.de/bird; www.lasers4life.de).

We invite highly motivated LMU students to join our team for a master’s thesis project. This opportunity focuses on applying machine learning and data analysis techniques to infrared spectroscopic data from human blood samples. The goal is to examine and advance infrared profiling for medical testing.

Our group is based at the Research Center in Garching (Forschungszentrum Garching, Section Physics of LMU, Am Coulombwall 1, 85748 Garching), accessible by public transport.

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