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- Tasks: Develop machine learning models to decode and engineer the immune system.
- Company: Join a cutting-edge biotech organisation focused on innovative biological solutions.
- Benefits: Enjoy flexible work options, collaborative culture, and opportunities for impactful research.
- Why this job: Be part of a mission-driven team tackling complex biological challenges with advanced technology.
- Qualifications: PhD or MSc in relevant fields; strong ML background and programming skills required.
- Other info: Ideal for those passionate about immunology and computational biology.
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
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 specialized LinkedIn groups can help you showcase your skills and connect with others who share your interests.
✨Tip Number 3
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.
✨Tip Number 4
Stay updated on the latest research and advancements in machine learning and immunology. Reading recent publications and following key researchers can provide insights that may be beneficial during interviews.
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 Compelling 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 the role, such as those involving machine learning applications in biology or immunology.
Demonstrate Technical Skills: Clearly outline your proficiency in Python and any experience with TensorFlow or PyTorch. If applicable, provide examples of projects where you applied these skills, especially in relation to biological data analysis or model development.
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 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 Problem-Solving Questions
Expect questions that assess your problem-solving skills in computational biology. Be ready to walk through your thought process on how you would approach analysing complex biological data or optimising ML pipelines.
✨Familiarise Yourself with Their Work
Research the company's recent projects and publications in immunology and biologics. Being knowledgeable about their work will not only impress your interviewers but also help you tailor your responses to align with their goals.
