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Bioinformatician

Bioinformatics Overview 3

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Computational Biology and Bioinformatics (M.Sc.) , University of Göttingen

This is my review of the master’s courses at the University of Göttingen.

Generally, there isn’t much going on in the summer…

  1. Machine Learning (6 C): It is definitely an important course, full of useful stuff. You need to do exercises (in groups of three people) for the exam, but the exam has nothing to do with them; it is just the math of ML.overall score: 8/10.
  2. Bioinformatik der Systembiologie (12 C): It is a really great course, containing one course, one seminar in which you need to present a paper, and an internship that you must complete with one of the PhD candidates in the group. Overall score: 9/10.
  3. Deutschkurs: Deutsch - Grundkurs 2 (A1.2.): This course is ideal for those who don’t speak German. Almost everyone achieves a good grade, but it’s categorized as a key competency and doesn’t count towards the GPA. Overall score: 6/10.

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PeriodTime / wkAdvanced programmingSoftware devRLBayesianDeliverable
Q1 (Sep–Nov 2025)11h (4/3/2/2)Python internals (C-API, buffer protocol), NumPy/SciPy memory model; vectorization & numba; pick C++20+pybind11 or Rust+pyo3 for extensions; profiling basics (cProfile, perf)Git strategies, pytest + coverage, mypy/ruff, packaging with poetry/pdm; CLI design; docs with Sphinx/MkDocs; CI via GitHub Actions & pre-commitRefresher: MDPs/bandits; implement tabular DP/MC, ε-greedy; Gymnasium intro; clean-from-scratch codeProbability refresher; conjugate models; prior/posterior predictive checks; PyMC or Stan basics; ArviZ diagnosticsTiny PyPI package (bio kernel or parser), benchmarks + doc site
Q2 (Dec 2025–Feb 2026)10h (4/2/2/2)Native acceleration: C++/Rust ext modules; cache-aware data layouts; PyTorch custom ops shim; flamegraphs; microbenchmarks; intro GPU via Triton or CUDA kernelsRepro/data: DVC for datasets, dataset cards; semantic versioning; release wheels (Linux/Mac)Policy Gradient (REINFORCE) from scratch; advantage baselines; experiment tracking (MLflow/W\&B)Hierarchical models; GLMs; HMC/NUTS tuning; LOO/WAIC model comparisonAccelerated op (e.g., k-mer tally/UMI dedupe) as a PyTorch/JAX extension + reproducible DVC pipeline
Q3 (Mar–May 2026)11h (3/3/3/2)GPU depth: memory coalescing, warp/wavefront basics; streams & async; JAX jit/pjit mental modelWorkflow engines: Snakemake/Nextflow + Docker; config mgmt with Hydra; API sketch with FastAPIValue-based RL: DQN (clean-room), target nets, replay buffers; sanity-check OOD & reward scalingGaussian Processes for time-series expression; sparse/inducing points; calibrationEnd-to-end pipeline (Nextflow) producing features → API serving a GPU-accelerated op; DQN repo with reproducible results
Q4 (Jun–Aug 2026)10h (3/2/3/2)HPC touches: SIMD/AVX, OpenMP; SLURM; distributed training intro (FSDP/torch.distributed)Observability: metrics/logging/tracing; perf budgets; simple K8s deploy or autoscaling containerPPO/A2C with robust training loops; eval protocol; basic safe/constrained tricksBayesian deconvolution for multi-omics; VI/ADVI; prior sensitivity & SBCYear-capstone: open-source “fast-omics-kernels” + preprint-style tech report OR “RL-guided assay selection (sim)” with Bayesian uncertainty; public demo & docs

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