Students projects

We have 6 openings for students projects :

1. Master thesis position:

Development of a DLD-Based Platform for Enrichment of Cell-Cell Pairs

This master thesis project focuses on the development of a Deterministic Lateral Displacement (DLD) platform specifically designed to enrich cell-cell pairs, particularly in the context of immuno-receptor driven interactions. The aim of this project is to build on a DLD device previously developed in our laboratory and adapt it to selectively enrich for cell doublets, thereby increasing the frequency of relevant interactions before downstream analysis.

FACS isolation is currently used to obtain cell pairs based on cell-specific labelling. However, the shear forces experienced by cells results in undesirable losses to overall efficiency from pair separation. Similarly, conventional filtering approaches using meshes and membranes have not provided sufficient resolution or recovery. DLD, by contrast, offers an elegant, label-free approach with the potential speed and precision to isolate doublets (~20 µm) from single cells, potentially allowing for FACS to be performed only on enriched populations, or even bypassed entirely if performance is sufficient.

The thesis will involve redesigning and optimizing the DLD platform to handle the unique size and mechanical characteristics of cell pairs. Key objectives include tuning the critical sorting size, adjusting array geometries, and minimizing shear-induced disruption of doublets during flow. A substantial component of the project will focus on validating post-sorting performance: quantifying enrichment efficiency, doublet integrity, and comparing the workflow to conventional FACS-based protocols.

The student will work closely with both the Laboratory of Life Sciences Electronics (CLSE-EPFL) and the Laboratory of Biomedical Microfluidics (LBMM), combining microfluidic design, and biological validation to iteratively improve the system’s performance for cell-pair enrichment.

Type of work: 10% literature study, 20% design and microfabrication, 25% cell culture and biological characterization, 25% microfluidic device testing, 20% data treatment and results reporting

Duration: 6 months

Prerequisites: Ideally, someone with a background in microtechnology, bioengineering or biology, but physics or mechanics are also welcome. A strong motivation is required regardless your background.

Do not hesitate to contact us by email in case of interest: [email protected], [email protected]

2. Master thesis position:

Bulk, chemical-free demulsification of water-in-oil microfluidic droplets by electromagnetic manipulation

Water-in-oil (W/O) microfluidic droplets are extensively used in single-cell genomics/transcriptomics, directed evolution, and diagnostic assays. A crucial step following droplet-based incubation or reaction is the recovery of the aqueous contents (e.g., cells, nucleic acids, proteins) for downstream analysis. Current methods predominantly rely on chemical surfactants to break the emulsion. However, these chemicals can interfere with sensitive molecular biology assays (like PCR or enzymatic reactions) and may affect the viability or integrity of recovered cells, limiting the scope and accuracy of subsequent analyses. Therefore, a gentle, rapid, and chemical-free method for droplet demulsification is highly desirable.

 We propose to develop and validate a novel approach for the bulk, chemical-free demulsification of W/O droplets using electromagnetic fields. This project aims to design, build, and test an electrical apparatus capable of inducing controlled droplet coalescence in a dedicated module, enabling efficient recovery of droplet contents without chemical additives. The system will exploit the dielectric properties of the oil and water phases to generate forces that destabilize the droplet interface upon application of specific electric fields. The project will involve investigating optimal electrode geometries and electrical parameters (voltage, frequency, waveform) to maximize coalescence efficiency while ensuring the integrity of biological samples potentially contained within the droplets.

Expertise Gained:
The project will give the student the opportunity to gain expertise in:

  • Microfluidic device design and fabrication/prototyping
  • Electronic system design and integration for device actuation
  • Experimental methods in microfluidics (droplet generation, manipulation)
  • Microscopy and image analysis
  • Basic cell handling and molecular biology techniques for validation

Type of work: 10% literature study, 25% design, simulation, and prototyping, 40% experimental work and device testing, 25% data analysis and results reporting.

Duration: 4 – 6 months

Prerequisites:

  • Ideally someone with a background in Electrical Engineering, Physics, Microengineering, Mechanical Engineering, or Bioengineering with relevant hands-on experience.
  • Familiarity with prototyping, electronics, laboratory instrumentation (power supplies, oscilloscopes), and CAD software.
  • A strong motivation and a hands-on, problem-solving attitude are required regardless of the specific background.

Do not hesitate to contact us by email in case of interest: [email protected], [email protected]

3 · Optimization of Microfluidic Chips for Light-Sheet Imaging(Master/Bachelor/Semester Thesis)

Short abstract

Design, fabricate, and benchmark precision-machined/3D printed micro-channel molds that maintain monodisperse two- and three-phase aqueous droplets as they traverse light-sheet illumination. Students will iterate channel geometries, optimize surface treatment, and quantify optical performance, directly feeding improvements into the droplet-LSFM drug-screening pipeline for personalized medicine.

Key words

Microfluidics channel, clean room, PDMS, CNC / 3D printing, 3D modeling, light-sheet microscopy

Description

What you will do

  1. Geometry scouting – review best-practice bend and expansion designs for segmented flow; select two candidates for straight, U-bend, and serpentine paths.
  2. CAD & fabrication – convert designs to aluminum or brass master molds using high-speed CNC micromilling or improve plastic SLS printing, measure surface roughness if possible.
  3. PDMS casting & bonding – cast 10:1 PDMS, embed glass side windows, and plasma-bond to #1.5 coverslips; verify dimensional fidelity under a microscope.
  4. Surface treatment – compare Aquapel and PEG-silane coatings for fluorinated-oil wetting; quantify contact angles and droplet lubrication film thickness.
  5. Optical benchmarking – flow 0.5 µL aqueous droplet separated by FC-40 oil; image 200 nm fluorescent beads with the LSFM; use the LSFM to record droplet outline and internal particle tracers. Quantify:
    1. plug volume coefficient of variation
    2. deformation index through 90 degree bends
    3. internal recirculation velocity
    4. residual stripe intensity (optional comparison with existing chips)

Skills you will gain
Clean-room mold fabrication, 3D modeling, droplet microfluidics, and hands-on light-sheet microscopy.

Goal

Produce a geometry-guided segmented-flow LSFM chip that reaches publication-grade optical quality and becomes the new standard cartridge for droplet-based drug screening.

Contact Details

If you are interested, please send your CV and transcript of records to Tianhao Li, [email protected]

4 · Optimizing Dye Panels for High-Throughput Light-Sheet Fluorescent Microscopy of Cancer Cells(Master/Bachelor/Semester Thesis)

Short Abstract

Develop a streamlined 4-color Cell-Painting dye set optimized for live 3-D light-sheet fluorescence microscopy (LSFM). You will screen >10 commercially available organelle probes, check their incremental information content, and document protocols that balances phenotypic richness, photostability, and spectral separation.

Key Words

Cell Painting · multiplex fluorescence · organelle probes · live-cell imaging · Light-sheet microscopy · phenotypic profiling

Description

Fluorescence Cell-Painting typically relies on 4-6 dyes that were tuned for 2-D wide-field plates. In fast LSFM volumes, however, overlap in excitation/emission make 4 channels sometimes difficult.
Your mission is to find the 4-dye combination that still captures most of morphological variance:

  1. Literature scan & vendor ordering – select ~10 stains (ER-Tracker Red, MitoTracker Deep Red, Lysotracker Green, DRAQ7, SiR-Tubulin, etc.) from recent multiplex-imaging studies
  2. Factorial screen – build a 4 × N dye matrix (405/488/561/640 nm lines) and image live/dead Jurkat cells.
  3. Calibration file – document laser power, exposure, and emission filters; supply Fiji macros and a Python notebook for reproducibility.
  4. Benchmark – compare photostability and cytotoxicity against standard Hoechst/Calcein/SiR panels

Skills you will gain

Live cell staining, morphological analysis, FIJI/python image processing, hands-on light-sheet microscopy.

Goal

Generate a validated, cost-efficient dye panel that maximizes morphological diversity while remaining fully compatible with high-speed droplet-LSFM drug screens.

Contact Details

If you are interested, please send your CV and transcript of records to Tianhao Li, [email protected]

5 · Light-Sheet Microscopy Live Wire: Real-Time Capture & Cluster-Scale Processing Pipeline(Master/Bachelor/Semester Thesis)

Short Abstract

Build a “zero-latency” data path that streams raw light-sheet stacks from our pyMMCore+ acquisition GUI directly into the EPFL HPC cluster, bypassing local disks and triggering automatic denoising / segmentation jobs. The student will redesign the GUI with a stable backend, implement live detection + OME-Zarr chunking, and benchmark throughput in a long acquisition run.

Key Words

Software hardware integration · Real-time streaming · HPC workflow · Python · pyMMCore+ · Light-sheet microscopy

Description

Modern light-sheet experiments can generate almost 1 GB s⁻¹; writing to local SSDs then copying to the cluster is the current bottleneck.
Your mission is to create a fully streaming acquisition/processing pipeline:

  1. GUI refactor – Improve the current light-sheet microscope control software based on python. Extend the existing pyMMCore+ control panel to a producer-consumer pattern using Qt signals & slots; camera frames land in a bounded queue while a background worker forwards them over.
  2. On-the-fly preprocessing – integrate a lightweight bead-based focus detector and write data directly to cloud-optimized OME-Zarr chunks.
  3. Cluster hand-off – when a new Zarr folder appears on the NAS3 mount, launch jobs for denoising and Cellpose segmentation.
  4. Benchmark – Run real acquisition to benchmark sustained throughput (MB s⁻¹) and end-to-end latency. Profile CPU/GPU utilization.
  5. Robustness – apply standard producer–consumer stress tests to verify loss-free acquisition beyond 60 min runs.

Skills you will gain

Python image processing/ machine learning pipeline, soft-hardware integration, hands-on light-sheet microscopy.

Goal

Remove the disk bottleneck and enable real-time analytics for high-throughput droplet-LSFM drug screens.

Contact Details

If you are interested, please send your CV and transcript of records to Tianhao Li, [email protected]

6 · Cell Segmentation and Classification Pipeline of Light-Sheet Imaging Data for Personalized Cancer Therapy(Master/Bachelor/Semester Thesis)

Short Abstract

Benchmark and integrate the most effective 3-D deep-learning pipelines for nucleus + membrane segmentation in light-sheet volumes, then build a lightweight classifier that labels each segmented object by cell type and treatment status. The outcome is a fully reproducible Jupyter Book, complete with trained weights and evaluation scripts, ready to drop into the droplet-LSFM screening workflow.

Key Words

3-D segmentation · StarDist 3D · Cellpose 3D · CellSeg3D · 3-D U-Net · napari annotation · self-supervised learning · Light-sheet microscopy

Description

Accurate 3-D cell masks are the foundation of every downstream morphology metric.
Your mission is to find, document, and package the best segmentation + classification stack for our multicolor LSFM data:

  1. Ground-truth creation — annotate cells in napari using nuclei + membrane channels as guidance.
  2. Model zoo benchmark — train and evaluate StarDist 3D, Cellpose 2.0 3D, PlantSeg3D, and a lightweight 3-D U-Net on identical splits; measure mean IoU and inference speed.
  3. Self-/semi-supervised add-ons — test at least one self-supervised approach to assess robustness when manual labels are scarce.
  4. Cell-type classifier — extract shape + intensity features and compare supervised versus self-supervised embeddings for live/dead cell labels.
  5. Packaging — wrap the final pipeline in a Jupyter Book tutorial; export PyTorch weights and a template for cluster execution.

Skills you will gain
Machine learning image analysis pipeline, and hands-on light-sheet microscopy.

Goal

Deliver a one-click 3-D segmentation + cell-type labeling module that slots into the droplet-LSFM analysis workflow.

Contact Details

If you are interested, please send your CV and transcript of records to Tianhao Li, [email protected]