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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 43200 - 72000 £ 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
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
Network with professionals in the biotech and computational biology fields. Attend relevant conferences or webinars where you can meet potential colleagues and learn about ongoing projects. This can give you insights into the company culture and current challenges they face.
✨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. Having a portfolio of practical applications can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your previous work in detail, particularly any projects related to immunology or biological data analysis. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will highlight your problem-solving skills.
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 how your background in machine learning and statistical modelling can contribute to decoding and engineering the immune system. Be specific about your experiences that relate to the job.
Showcase Your Projects: If you have worked on relevant projects, especially those involving machine learning models or biological data analysis, summarise these in your application. Highlight any publications or presentations that demonstrate your scientific productivity.
Proofread and Edit: Before submitting your application, carefully proofread all documents for spelling and grammatical errors. Ensure that your writing is clear and concise, as this reflects your attention to detail and professionalism.
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, and 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 projects where you successfully collaborated with others. Emphasise your ability to communicate complex ideas clearly to both technical and non-technical stakeholders.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your problem-solving skills in computational biology. Practice articulating your thought process when tackling complex datasets or developing new computational methods.
✨Familiarise Yourself with Current Trends
Stay updated on the latest advancements in machine learning applications within immunology and biologics. Being able to discuss recent research or breakthroughs can demonstrate your passion and commitment to the field.
