The Applied Research Scientist role combines elements of research and practical application. Applied Research Scientists will focus on leveraging their deep understanding of scientific principles and research methodologies to address real-world problems and develop innovative products in areas such as protein structure prediction, generative models for structure and sequence, molecular dynamics, protein-protein interactions, and more.
ROLE RESPONSIBILITIES:
- Design and implement proof of concept methods as well as benchmarking with state-of-the-art methods.
- Leverage classical and/or deep learning biology tools and large biological databases to perform protein design, sequence optimisation, molecular modelling, and other relevant tasks.
- Report and present experimental results and research findings, both internally and externally, verbally and in writing.
- Upon request, collaborate with other groups' activities, including but not limited to presenting the company to new prospective clients, participating in calls and meetings.
SPECIFIC AREAS OF INTEREST: Computational Modelling:
- Applied research scientists (ARS) often use computational (statistical, machine learning and/or deeplearning) modelling techniques to complement experimental data and perform in silico macromolecular simulations.
- Proteomics: ARS develop innovative approaches to leverage language and structure models to extract meaningful information from proteomics datasets, including mass spectrometry data, protein-protein interaction networks, and protein sequence data. Their responsibilities include designing and implementing computational pipelines to preprocess and analyze proteomic data, fine-tuning deep learning models for specific proteomics tasks such as protein function prediction or post-translational modification identification, and integrating model outputs with other experimental or computational data to gain deeper insights into complex biological systems.
- Genomics/Transcriptomics: ARS employ advanced sequencing technologies and computational tools to analyze large-scale genomic and transcriptomic datasets. Their responsibilities include designing and executing experiments to generate high-quality sequencing data, developing and implementing data analysis pipelines to identify genetic variations, gene expression patterns, and regulatory networks, and interpreting the results to uncover meaningful biological findings.
- Protein Engineering: ARS study the sequence-structure-function relationship. They apply their knowledge to optimize the binding affinity, selectivity, reactivity and/or efficacy of putative therapeutic designs. They also work on designing and modifying protein sequences to enhance their stability, activity, or other desired properties.
REQUIREMENTS
- PhD in Computational Biology, Machine Learning or a related scientific field or equivalent industry experience.
- A strong foundation in biology, biochemistry, and/or biophysics. They understand the fundamental principles of molecular biology and are familiar with the structure-function relationships of biological macromolecules.
- Relevant experience in the application of classical and deep learning algorithms in any of the following domains is highly desirable:
- Structural biology (e.g. protein structure prediction)
- Protein language models
- Multi-omics
- Molecular dynamics
- Protein-protein interactions
- Methods for handling 3D structural data, such as Graph Neural Networks.
- Generative ML models
- The ideal candidate must have extensive experience working with large biological datasets, databases (PDB, Uniprot, etc.) and their API technologies.
- A demonstrated ability to successfully deliver high-quality research, for example through the publication of scientific papers in journals or conferences.
- Excellent communication skills and collaborative spirit.
- Software development skills in Python are necessary.
- Experience using deep learning frameworks such as PyTorch, JAX and/or TensorFlowis a plus.
- Wet lab experience specifically in the areas of biophysical assay development, protein purification and mRNA manipulation is a plus.