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- Tasks: Develop machine learning models to analyse complex biological data and drive innovative projects.
- Company: Join a cutting-edge biotech firm dedicated to decoding the immune system.
- Benefits: Enjoy flexible work options, competitive salary, and opportunities for professional growth.
- Why this job: Be part of a collaborative team making impactful contributions to healthcare through technology.
- Qualifications: PhD or MSc in relevant fields with strong machine learning and statistical modelling skills required.
- Other info: Experience with TensorFlow/PyTorch and GPU computing is a plus.
The predicted salary is between 36000 - 60000 £ per year.
A cutting-edge biotech organization is seeking highly motivated Computational Scientists to support the mission of decoding and engineering the immune system. The role focuses on developing advanced machine learning and statistical models to analyze complex biological data, particularly immune repertoires and multimodal datasets.
About the Role
- Design and implement machine learning models—particularly language models, diffusion models, or graph neural networks—tailored to biomedical challenges.
- Build novel computational methods for interpreting biological sequences and structural data.
- Customize existing tools and develop new ones for integrative analysis and visualization of large-scale systems immunology data.
- Drive ML-based pipelines for diagnostic or therapeutic design.
- Benchmark computational methods and optimize performance across datasets.
- Lead or contribute to collaborative projects spanning academic, clinical, and industry domains.
Required Qualifications
- PhD (or MSc with equivalent experience) in Computational Biology, Bioinformatics, Computer Science, Statistics, Physics, or related quantitative discipline.
- Strong background in machine learning and statistical modeling, with a demonstrated ability to solve complex biological problems.
- Proven track record of scientific productivity (e.g., peer-reviewed publications).
- Hands-on experience in data handling, visualization, and biological data analysis.
- Proficient in Python, familiar with software development best practices.
- Practical experience with TensorFlow and/or PyTorch.
Preferred Qualifications
- 3+ years post-graduate experience in academia or biotech/pharma, applying ML/AI to biological datasets.
- Prior exposure to immunology, especially TCR/BCR repertoire analysis, or experience with protein design & or biologics.
- Deep expertise in at least one of the following areas:
- Language models for sequence analysis
- Diffusion models in molecular design
- Graph ML in biomedical networks
Computational Biology & Machine Learning Scientist employer: Skills Alliance
Contact Detail:
Skills Alliance Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Computational Biology & Machine Learning Scientist
✨Tip Number 1
Network with professionals in the biotech and computational biology fields. Attend relevant conferences, webinars, or workshops to meet potential colleagues and learn about the latest trends in machine learning applications in immunology.
✨Tip Number 2
Engage with online communities and forums focused on computational biology and machine learning. Platforms like GitHub or LinkedIn can be great for showcasing your projects and connecting with others who share your interests.
✨Tip Number 3
Stay updated on the latest research in machine learning and immunology. Reading recent publications and following key researchers can provide insights into the skills and knowledge that are currently in demand.
✨Tip Number 4
Consider contributing to open-source projects related to machine learning in biology. This not only enhances your skills but also demonstrates your commitment and expertise to potential employers.
We think you need these skills to ace Computational Biology & Machine Learning Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in computational biology, machine learning, and any specific projects related to immunology or biologics. Use keywords from the job description to align your skills with what the company is looking for.
Craft a Strong Cover Letter: In your cover letter, express your passion for the role and the company's mission. Discuss your experience with machine learning models and how it relates to the challenges mentioned in the job description. Be specific about your contributions to past projects.
Showcase Your Publications: If you have peer-reviewed publications, mention them in your application. Highlight any that are particularly relevant to computational biology or machine learning, as this demonstrates your scientific productivity and expertise in the field.
Highlight Technical Skills: Clearly outline your technical skills, especially in Python, TensorFlow, and PyTorch. If you have experience with GPU computing or specific machine learning techniques like language models or graph neural networks, make sure to include these details.
How to prepare for a job interview at Skills Alliance
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning models, particularly language models, diffusion models, or graph neural networks. Highlight specific projects where you've applied these techniques to solve biological problems.
✨Demonstrate Your Collaborative Spirit
Since the role involves working within a team, share examples of past collaborative projects. Emphasise your ability to work across academic, clinical, and industry domains, showcasing how you contributed to team success.
✨Prepare for Technical Questions
Expect questions that test your knowledge of computational methods and statistical modelling. Brush up on your understanding of data handling, visualisation, and analysis of biological datasets, as well as your proficiency in Python and frameworks like TensorFlow or PyTorch.
✨Discuss Your Research Impact
Be ready to talk about your scientific productivity, including any peer-reviewed publications. Discuss how your research has contributed to the field of computational biology and how it aligns with the company's mission to decode and engineer the immune system.
