Genenet Technology Ltd.

Cambridge, UK

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LIAISON

Ming-Tsung (Louis) Wu

ORGANIZATION ADDRESS

BIo-Incubator, Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK

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traineeship Info

Internship Info

ORGANIZATION INFO

Genenet Technology is a company integrating state-of-art deep learning neural networks into gene circuits. Our patented technology can be applied to any field that needs genetic engineering (e.g. medicine, synthetic biology, and pharmaceutical industries). We plan to offer genetic circuit design service, consultation service, and patent licensing to academic or industrial research units. We are now working on applications including increasing PHA bioplastic production, CHO cell bioproduction yield and using cardiac organoids for drug discovery.

MENTOR(S) FROM THE ORGANIZATION

Ming-Tsung (Louis) Wu

Adjunct Assistant Professor, Institute of Plant Biology, NTU / Visiting Assistant Research Fellow, Center for Developmental Biology and Regenerative Medicine, NTU / Chairman, National Taiwan University Alumni Association in the UK

NTU

Alumni

VACANCIES

Mentor(s)/ Co-supervisor(s)

Ming-Tsung (Louis) Wu

co-supervisor

Haydn Cheong

Machine learning Engineer

co-supervisor

Brief Introduction of the Department

Department Staff Number

Less than 5 people

Internship Job Description

At Genenet Technology, we are building the next-generation platform to turn cells into AI computers by developing cutting-edge technology in the biological domain. Our goal is to empower other like-minded and passionate individuals to contribute to this mission.

We are seeking a Biomedical AI/ML Engineer Intern to accelerate our design of novel AI solutions for the life sciences industry. This person will work closely with our leadership and research teams to impact the full product development cycle — from identifying biological and experimental needs, brainstorming innovative solutions, to developing scalable AI models tailored for complex biomedical problems.

We are looking for someone who is curious, technically strong, and eager to bridge the gap between biology and computation.

Responsibilities:

Design, implement, and deploy AI/ML-driven solutions across the full model development lifecycle
Research and develop state-of-the-art deep learning models to optimize performance on biomedical prediction and analysis tasks
Collaborate with wet-lab scientists to integrate experimental data, including molecular and cellular biology results, into AI workflows
Apply image processing and computer vision methods to analyze microscopy and biological imaging data
Work with single-cell and spatial transcriptomics datasets to extract meaningful biological insights
(Optional) Explore the use of quantum computing approaches to accelerate data analysis and model optimization
Deliver robust, tested, and maintainable code in an agile, research-driven environment
Communicate progress clearly through reports and presentations

Preferred Intern Education Level

Master students
Doctoral students

Preferred Intern Skill Sets or Qualities

Experience with image analysis, microscopy data, or biomedical imaging
Familiarity with single-cell or spatial transcriptomics data analysis workflows
Understanding of molecular and cellular biology, or willingness to learn through collaboration with wet-lab researchers
Experience or interest in quantum computing for data analysis or optimization
Strong programming skills (e.g., Python, TensorFlow, PyTorch) and solid knowledge of ML concepts
Interest in interdisciplinary research connecting AI, quantum computing, and biotechnology

Internship Period

From June 15 to August 31, 2026 (adjustable at the discretion of the organisation)

Others

Expected working hours per week: 40 hours
Salaries (stipends) will be provided
Benefits will be provided
The organization will cover the Certificate of Sponsorship (CoS) costs. Students are responsible for the VISA application fee.
To obtain a VISA, the intern must possess an English Language Test Certificate with a score equivalent to:

IELTS (Academic or General Training): Band 5.5 or higher
CEFR Scale: B2, C1, or C2 from Language Cert, Pearson, PSI Services, TOEIC, or TOEFL
Expired Certificates: Expired certificates are acceptable as long as the expiry date falls within 24 months of the visa application date.
No English Language Test Certificate: If you do not have an existing English Language Test Certificate, NTU OIA can provide you with a link to take an online test. Results from this test will be available within 3 business days.

Scholarship

Mentor(s)/ Co-supervisor(s)

Ming-Tsung (Louis) Wu

co-supervisor

Haydn Cheong

Machine learning Engineer

co-supervisor

Brief Introduction of the Department

Department Staff Number

Less than 5 people

Internship Job Description

At Genenet Technology, we are building the next-generation platform to turn cells into AI computers by developing cutting-edge technology at the interface of biology and computation. Our goal is to empower other like-minded and passionate individuals to contribute to this mission.

We are seeking an Lab Scientist to accelerate our progress in the wet lab using the power of AI and machine learning. This person will work closely with our lead scientists to discuss hypotheses, propose digital solutions for ongoing investigations, and — most importantly — design and perform molecular and cellular biology experiments aligned with our core research agenda.

We are looking for someone who is scientifically curious, digitally creative, technically strong, and unafraid to ask questions.

Responsibilities include:

Conduct cell-based and molecular biology experiments (e.g., cell culture, transfection, gene expression assays, microscopy) to validate and guide AI-driven hypotheses
Collaborate with both the AI and wet lab research teams to optimize biological processes and experimental design
Develop and apply AI / ML models to analyze and model biological data
Automate or digitize experimental procedures where possible to improve efficiency
Write technical reports and presentations to communicate progress with colleagues and management
Follow project timelines and proactively identify challenges or technical issues‍

Preferred Intern Education Level

Master students
Doctoral students

Preferred Intern Skill Sets or Qualities

Familiarity with a quantitative or engineering discipline (computer science, computational biology, bioinformatics, engineering, among others); OR equivalent additional work experience conducting experiments in the wet lab
Familiarity with machine learning literature, foundational mathematics and statistics, and state-of-the-art modeling approaches
Previous experience in designing, developing, and delivering robust solutions to optimize biological processes

Additional Bonus

Contributions to relevant open-source projects
Relevant publication record
Experience working with biomedical data
Experience working in wet lab settings

Internship Period

From June 15 to August 31, 2026 (adjustable at the discretion of the organisation)

‍

Others

Expected working hours per week: 40 hours
Salaries (stipends) will be provided
Benefits will be provided
The organization will cover the Certificate of Sponsorship (CoS) costs. Students are responsible for the VISA application fee.
To obtain a VISA, the intern must possess an English Language Test Certificate with a score equivalent to:

IELTS (Academic or General Training): Band 5.5 or higher
CEFR Scale: B2, C1, or C2 from Language Cert, Pearson, PSI Services, TOEIC, or TOEFL
Expired Certificates: Expired certificates are acceptable as long as the expiry date falls within 24 months of the visa application date.
No English Language Test Certificate: If you do not have an existing English Language Test Certificate, NTU OIA can provide you with a link to take an online test. Results from this test will be available within 3 business days.

Scholarship

ORGANIZATION ADDRESS

BIo-Incubator, Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Puddicombe Way, Cambridge CB2 0AW, UK