ppAdjunct Professor of Machine Learning /p h3IE University - School of Science Technology /h3 pbLocation: /b Madrid / Segovia (on-campus, live in-person teaching) /p pbContract: /b Adjunct / part-time faculty (per-course) /p pbLanguage of instruction: /b English /p pbStart: /b September 2026 /p h3About IE University /h3 pIE University's School of Science Technology brings together computer science, artificial intelligence, mathematics and engineering in a teaching model that is collaborative, active and applied. Our students build knowledge by doing, working through real problems in individual and group settings. We are looking for a practitioner-scholar in machine learning who can bring both technical depth and hands‑on industry perspective to the classroom. /p h3The role /h3 pWe are hiring an adjunct professor to teach three groups across two compulsory third-year courses: /p ul libOne group of AI - Machine Learning Analytics /b (6 ECTS, 30 live in-person sessions). Part of the Dual Degree in Business Administration Data and Business Analytics (BBADBA). /li libTwo groups of AI - Reasoning Problem Solving /b (6 ECTS, 30 live in-person sessions each). Part of the Bachelor in Computer Science and Artificial Intelligence (BCSAI). /li /ul pEach course carries a teaching load of 30 sessions and a total student workload of 150 hours. All teaching is delivered in English. /p h3AI - Machine Learning Analytics /h3 pA "from theory to practice" course covering the foundations of modern AI. The syllabus moves from classical machine learning on tabular data (supervised and unsupervised methods, random forests, XGBoost, clustering, recommender systems) through deep learning and computer vision (fully connected and convolutional networks, transfer learning, generative models such as autoencoders,



GANs and diffusion) to natural language processing and LLMs (transformers, prompt engineering, API integration, Gradio, LangChain). Fairness and ethics in AI are introduced early. Teaching is hands‑on, built around Jupyter notebooks, Python (scikit-learn, TensorFlow/Keras), Kaggle competitions and a group project. /p h3AI - Reasoning Problem Solving /h3 pAn introductory course to the foundations of AI: intelligent agents, problem solving and search (uninformed, informed and local search), search in complex and non-deterministic environments, adversarial search and games (minimax, alpha-beta, Monte Carlo Tree Search, game theory), constraint satisfaction problems, knowledge representation and reasoning (logical agents, first-order logic, planning), reasoning under uncertainty, probabilistic programming, and reasoning in LLMs. Students implement search algorithms, CSP solvers and game‑playing agents in Python through individual and group assignments. /p h3Responsibilities /h3 ul liDeliver live in-person lectures and coding sessions for the three assigned groups, following the established course syllabi. /li liPrepare and run practical assignments, in-class exercises and Jupyter‑notebook demonstrations. /li liSet, supervise and grade assignments, quizzes, exams and group projects in line with each course's evaluation criteria. /li liProvide student guidance and hold office hours on request.



/li liUphold IE University's attendance, academic integrity and ethics policies. /li /ul h3Requirements /h3 ul liPhD (or near completion) in computer science, artificial intelligence, mathematics, statistics, physics or a closely related field. An exceptional professional track record may substitute for the doctorate. /li liStrong command of machine learning, deep learning and classical AI (search, reasoning, agents). /li liProficient in Python and its ecosystem (scikit-learn, TensorFlow/Keras, pandas, and standard AI/ML libraries). /li liDemonstrated ability to teach technical material to a mixed audience, including business‑track students in the analytics course. /li liFull professional fluency in English. /li liEligibility to work in Spain. /li /ul h3Desirable /h3 ul liIndustry or applied research experience in AI/ML that can be brought into the classroom. /li liFamiliarity with current LLM tooling and generative AI workflows (transformers, RAG, prompt engineering, Gradio, LangChain, Hugging Face). /li liPrior university teaching experience, ideally in an active, project‑based environment. /li liExperience supervising student projects delivered as working code (e.g. GitHub repositories). /li /ul h3What we offer /h3 ul liThe opportunity to teach at one of Europe's leading innovation‑focused universities. /li liA collaborative faculty community at the intersection of technology and business. /li liFlexible, per-course adjunct engagement that fits alongside professional or research commitments. /li /ul pIE University is committed to diversity and equal opportunity. We welcome applications from all qualified candidates regardless of background. /p /p #J-18808-Ljbffr

📌 Adjunct Professor (Madrid)
🏢 Ie School Of Science And Technology
📍 Madrid