Student Talents

We bring companies together with students by tackling data challenges in a hackathon style, provide collaborations for final thesis or offering key note speeches at our lecture for student recruiting

Data Challenge

Each semester, we host the advanced seminar “Data Analytics in applications” which is an interactive and interdisciplinary lecture for master´s students in a hackathon style over six months on a real-world data use case provided by the KI-Lab partners resulting in ready-to-use applications. 

Location recommendation and demand prediction for charging stations

Objective: Identification of locations for ultra-fast chargers based on usage forecasts.

Prediction of the power consumption of a plant


Objective: Improving the prediction of a facility’s power consumption.

Predicting leads based on cookie data

Objective: Predict whether a website visitor will become a potential customner(lead) and thus reduce the cost of customer retention and marketing expenditure.

Product demand forecasting to optimize sales & operations planning

Objective: Predict future market demand for >900 products for the next 18 months.

Net Promoter Score (NPS) driver identification

Objective: Analyze customer feedback to identify NPS drivers across different markets.

Detecting anomalous events in time series data

Objective: Prediction of country-concept-segment level for car sales and detection of anomalous events.

Location recommendation and demand prediction for charging stations

Objective: Identification of locations for ultra-fast chargers based on usage forecasts.

Prediction of the power consumption of a plant

Objective: Improving the prediction of a facility’s power consumption.

Predicting leads based on cookie data

Objective: Predict whether a website visitor will become a potential customner(lead) and thus reduce the cost of customer retention and marketing expenditure.

Product demand forecasting to optimize sales & operations planning

Objective: Predict future market demand for >900 products for the next 18 months.

Net Promoter Score (NPS) driver identification

Objective: Analyze customer feedback to identify NPS drivers across different markets.

.

Detecting anomalous events in time series data

Objective: Prediction of country-concept-segment level for car sales and detection of anomalous events.

Best practice sharing

We ensure continuous learning and best-practice transfer for our KI-Lab partners through workshops, webinars and startup matchmaking.

AI in Production

Smart products for end-customers

AI enabled digital services

TÜV_Süd_logo_negativ

Data & AI platforms for efficiency and sustainability

Data as an enabler for AI and analytics

AI Innovation ecosystem

AI in Production

Smart products for end-customers

AI enabled digital services

TÜV_Süd_logo_negativ

Data & AI platforms for efficiency and sustainability

Data as an enabler for AI and analytics

AI Innovation ecosystem

Sprint Projects

Depending on your current needs, you conduct an individualized
sprint project. It can be tailored to your current Data & AI maturity.

Development of a group-wide Gen.AI roadmap

Advancements of DAta, Anaytics & AI use case management

Leveraging Gen.AI potentials in software engineering

Customer Live Score

Building a process satisfaction prediction model for claims management using process mining and machine learning

Product mining for product-portfolio leanification

Framework development

Evaluating Post-hoc Explainable

AI Methods for validation in Automated Noise Detection

Development of a group-wide Gen.AI roadmap

Advancements of DAta, Anaytics & AI use case management

Leveraging Gen.AI potentials in software engineering

Customer Live Score

Building a process satisfaction prediction model for claims management using process mining and machine learning

Product mining for product-portfolio leanification

Framework development

Evaluating Post-hoc Explainable

AI Methods for validation in Automated Noise Detection

Academia and Practice

We advance industry transfer by advancing the 
exchange between research, and practice by 
conducting hands-on research problems, 
state-of-the-art AI tools and a knowledge platform
with a use case library.

BA Value

Decision models for BA projects

BA Success

BA implementation concept

resProKI

Resilient Production System with AI

AIMOB

AI in the Make or Buy Process

FlyNet

Establishing network structures for Industry 4.0

Smart.Office

Intelligent Energy Management

BigData

Analysis of Big Data-based business models

GenAIsis

Generative AI implementation concept

BA Value

Decision models for BA projects

BA Success

BA implementation concept

resProKI

Resilient Production System with AI

AIMOB

AI in the Make or Buy Process

FlyNet

Establishing network structures for Industry 4.0

Smart.Office

Intelligent Energy Management

BigData

Analysis of Big Data-based business models

GenAIsis

Generative AI implementation concept