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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 advanced 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
As part of a collaborative Computational Biology team, you will:
- 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
Familiarise yourself with the latest advancements in machine learning models, especially language models and graph neural networks. Being able to discuss recent research or breakthroughs in these areas during your interview can demonstrate your passion and expertise.
✨Tip Number 2
Engage with the computational biology community by attending relevant conferences or webinars. Networking with professionals in the field can provide insights into the latest trends and may even lead to referrals for job openings.
✨Tip Number 3
Showcase your hands-on experience with tools like TensorFlow and PyTorch by working on personal projects or contributing to open-source initiatives. This practical experience can set you apart from other candidates and highlight your technical skills.
✨Tip Number 4
Prepare to discuss specific examples of how you've applied machine learning to solve biological problems. Having concrete case studies ready will help illustrate your problem-solving abilities and your fit for the role.
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, particularly those relevant to biomedical challenges, and how you can contribute to their goals.
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.
Highlight Technical Skills: Clearly outline your proficiency in Python, TensorFlow, and PyTorch in your application. If you have experience with GPU computing or specific machine learning techniques mentioned in the job description, be sure to include those 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 collaborative team, share examples of past teamwork experiences. Discuss how you contributed to projects that spanned academic, clinical, and industry domains.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to tackle complex biological data challenges. Practice articulating your thought process when designing computational methods or optimising performance across datasets.
✨Familiarise Yourself with Relevant Tools
Make sure you're comfortable discussing your hands-on experience with Python, TensorFlow, and PyTorch. Be ready to explain how you've used these tools in data handling, visualisation, and analysis of biological datasets.
