Want to put your bioinformatics and data science skills to practical use? Three years post-launch, Aspect Analytics is entering a scale-up phase to keep up with the increasing demand for our products and services in spatial omics and mass spectrometry data analysis. Trailblazing life science innovators collaborate with us to gain maximal insights out of their complex data.
Aspect Analytics supports leading pharmaceutical, biotech and diagnostic companies in improving our understanding of biology to ultimately improve human health. Specifically, we develop novel bioinformatics solutions to catalyze the thriving field of spatial multi-omics, which aims at characterizing complex biological samples on a molecular level in spatially resolved ways. The primary application areas are in oncology, neuroscience and pharmaceutical research.
Description of the position
We are currently extending our team and platform, and we are looking for highly motivated people that want to learn more about mass spectrometry imaging or liquid-chromatography mass spectrometry data analysis. We are looking for someone who is willing to learn from our existing analysis models and pipelines, and extend or revise them based on a first-principles based approach, starting with your understanding of chemistry and physics. The output of your work will entail new models and pipelines. As part of your position, we will provide hands-on coaching to help you grow your skill set.
You will learn to:
Put your chemistry and physics knowledge into practice.
Learn about mass spectrometry imaging (MSI) and liquid-chromatography mass spectrometry (LC-MS) technologies.
Build models and pipelines from first-principles to process and analyze MSI and LC-MS data.
Reliably run these pipelines on production instances.
Good understanding of chemistry and physics to be able to:
Understand the principles behind mass spectrometry
Understand the principles behind liquid chromatography
Ability to reason about the molecular makeup of different types of polymers
Python programming experience
Experience working with large data in array format
Experience with libraries like Numpy, Scipy, Numba, or Dask
Experience working with array processing and automatic differentiation libraries such as Jax, PyTorch, TensorFlow, Theano, or Torch
Fluency in mathematics:
Understanding of gradient based optimization and its usage in maximum-likelihood estimation
Knowledge of (Approximate) Bayesian methods, such as Bayesian Inference, Monte Carlo Estimation, or Variational Inference
We work (at least partially) remotely, so good communication skills and an ability to work independently is important. An inquisitive mindset is key for these positions. You will receive direct training by seasoned machine learning experts in solving challenging real-world problems.
The envisioned start date for this position is July 2022.
We provide the possibility to work fully remote for exceptional candidates.