Associate Lecturer for Machine Learning

Pracovní nabídka

TU Prague City (https://www.tees.ac.uk/prague/) is looking for an Associate Lecturer for the Postgraduate Computing course, focusing on Machine Learning.

The module you will be teaching is MSc Machine Learning. You should have a good knowledge of the following key teaching areas:

• Probability estimation and sequence modelling (e.g. n-gram models, Hidden Markov models)
• Classification and regression techniques (e.g. naïve Bayes, decision trees, k-NN, linear and logistic regression)
• Clustering, anomaly detection, and dimensionality reduction
• Feature selection and model evaluation
• Reinforcement learning, planning, and learning
• Neural networks and deep learning
• Practical applications of machine learning in commercial and economic contexts

The module will also include elements of the following concerning the Professional & Ethical Context:

• Legal, professional, and ethical issues in machine learning
• Data protection and responsible data use
• Awareness of professional standards, including BCS codes of conduct and Practice

Required qualifications

• Relevant qualification in computer science, data science, machine learning, or a related field
• Strong subject knowledge in machine learning techniques
• Teaching or industry experience desirable

Responsibilities

• To prepare and deliver lectures and tutorials to undergraduate/postgraduate students as required.
• To carry out assessments and re-assessments, and submit marks to Module Boards as required
• To provide adequate and relevant assessment feedback to all students.
• Participation in the Quality Assurance activities as and when appropriate.
• Adhere to the TU PCSC’s Health and Safety Policy and guidelines.
• Promote Equality and Diversity for staff and students
• Any other reasonable duties that may be allocated from time to time

We Offer

An associate lecturer position within the faculty of Business and Computing.

Hours: 3 hours teaching contact hours per week
Salary: 9,900 CZK per month

Start Date: 16th February 2026

End Date: 12th June 2026

Timetable to be confirmed and negotiable.