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- Tasks: Develop machine learning models to decode and engineer the immune system.
- Company: Join a cutting-edge biotech organisation at the forefront of immunology research.
- 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 innovative technology.
- Qualifications: PhD or MSc in relevant fields; strong machine learning and data analysis skills required.
- Other info: Ideal for those passionate about applying AI in biology and making a real-world impact.
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 meetups to connect with potential colleagues and learn about the latest trends in machine learning applications in immunology.
✨Tip Number 2
Showcase your hands-on experience with Python and machine learning frameworks like TensorFlow or PyTorch. Consider contributing to open-source projects or creating your own projects that demonstrate your skills in developing models for biological data analysis.
✨Tip Number 3
Familiarise yourself with the latest research in immunology and biologics. Understanding current challenges and breakthroughs in the field will help you articulate how your skills can contribute to the company's mission during interviews.
✨Tip Number 4
Prepare to discuss specific examples of your past work related to machine learning and biological datasets. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will demonstrate your problem-solving abilities.
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 the role, especially those involving machine learning applications in biological contexts.
Demonstrate Technical Skills: Clearly outline your proficiency in Python and any experience with TensorFlow or PyTorch. If applicable, provide examples of projects where you've applied these skills, particularly in relation to data handling and analysis in a biological setting.
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 proficiency in Python, TensorFlow, and PyTorch. Be ready to explain how you've used these tools in your previous work, especially in data handling and visualisation.
