Computational Geneticist

Job Description

We are looking for a highly motivated statistical geneticist to work as part of the R&D team. The Computational Geneticist will investigate how the complex relationships between genetics and trait appearance in fungi. The Computational Geneticist will  The Computational Geneticist will be involved in the development of new methods that incorporate local ancestry and complex variants (TRs and HLA types) in multivariate models and polygenic risk scores (PRS) calculation, which will be benchmarked to determine accuracy across ancestries and admixed individuals.


  • Aid the design of wet-lab experiments to investigate novel and existing RNA regulatory mechanisms.
  • Analyze high-throughput drug and sequencing data (e.g. RNA-seq, CLIP-seq, MPRAs, Perturb-seq) to help build machine learning models.
  • Apply machine learning models to design novel antisense oligonucleotides.
  • Develop fast and accurate predictive workflows to support target validation.
  • Lead small teams of up to 3 colleagues
  • Develop and lead projects in pursuit of a specific goal

Experience, Qualifications, Skills and Knowledge

  • PhD in a relevant research area such as genetics, genomics, or bioinformatics or degree in computer science or biology with experience in bioinformatics.
  • Proven skills and experience in independently resolving broad computing/data / CI problems using introductory and/or intermediate principles.
  • Broad experience in one or more of the following: optimizing, benchmarking, HPC performance and power modelling, analyzing hardware, software, and applications for HPC / data / CI.
  • Proven experience and ability to collaborate effectively with all levels of staff; technical, students, faculty and administrators.
  • Intermediate knowledge of HPC / data science / CI.
  • Advanced skills, and demonstrated experience associated with one or more of the following: HPC hardware and software power and performance analysis and research, design, modification, implementation and deployment of HPC or data science or CI applications and tools.
  • Ability to contribute research and technical content to grant proposals.
  • Effective communication and interpersonal skills. Ability to communicate technical information to technical and non-technical personnel at various levels in the organization and to external research and education audiences.
  • Thorough experience working in a complex computing/data / CI environment encompassing all or some of the following: HPC, data science infrastructure and tools/software, and diverse domain science application base.
  • Proven ability to understand research computing/data / CI needs, mapping use cases to requirements and how systems/software/infrastructure can support those needs and meet the requirements. Ability to develop and implement such solutions.