TUM School of Management

Deep Tech LabGlobal Data on Deep Tech Ventures

First-of-its-kind data collection and scientific analysis of deep tech ventures worldwide.

Unique Dataset

First-of-Its-Kind Data Collection

Scientific identification of deep tech ventures vs. regular tech ventures through classification using scraped information on technology readiness, scientific novelty, and IP intensity.

Our work is harmonized: a global dataset enabling longitudinal comparisons across geographies and technology contexts.

Data visualization

Ventures

Comprehensive registry of deep tech startups globally.

IP & Patents

Patent portfolios and scientific publications of founding teams.

Founding Teams

Academic and industry backgrounds, multidisciplinary expertise.

Partnerships

Corporate collaborations, development partnerships, and investments.

Financing History

Grants, venture capital, corporate VC, hybrid financing models.

Spin-outs

University and research-institute spin-out identification.

Our Study in a Nutshell

Research Questions

Researchers with telescope

Descriptive Analyses

  • Mapping global deep-tech hotspots and their evolution over time.
  • Venture founding counts & strength of research institutions.
  • Density of corporate partners by industry.
  • Identification of industry and technology clusters (quantum, biotech, carbon capture…).

Founding Team Characteristics

  • Academic & industry backgrounds.
  • Multidisciplinary expertise and seniority.
  • Founding team size and complementarities.

Venture Characteristics

  • Business model archetypes.
  • Time to funding & capital intensity.
  • Regulatory exposure analysis.

Success Factors

  • Patents and founding-team publication history.
  • Role of spin-outs from universities.
  • Founding-team composition (scientific/industrial experience, disciplines, tenure).
  • Corporate partnerships (development collaborations, corporate investments).
  • Funding models / hybrid financing (grants, derisking, VC, corporate VC, infrastructure banks).
The Team

Our Researchers

Prof. Dr. Isabell M. Welpe

Prof. Dr. Isabell M. Welpe

Prof. Dr. Isabell M. Welpe is the Chair of Strategy and Organization at the Technical University of Munich. Her research focuses on leadership, the future of work, the impact of digitalization on organizations and companies, and strategic innovation.

welpe@tum.de
PD Dr. Theresa Treffers

PD Dr. Theresa Treffers

PD Dr. Theresa Treffers is a Privatdozentin (associate professor) at the Chair of Strategy and Organization. Her research interests lie at the intersection of business and psychology concepts in the areas of entrepreneurship, start-ups, DEI (Diversity, Equity & Inclusion), as well as strategy and innovation.

theresa.treffers@tum.de
Jannik Nolden

Jannik Nolden

Jannik Nolden is a doctoral candidate at the Chair of Strategy and Organization. His research focuses on deep tech entrepreneurship and startup unicorns.

jannik.nolden@tum.de
Jakob Kröner

Jakob Kröner

Jakob Kröner is a doctoral candidate at the Chair of Strategy and Organization. His research focuses on deep tech entrepreneurship.

jakob.kroener@tum.de
Philipp Schaffer

Philipp Schaffer

Philipp Schaffer is a doctoral candidate at the Chair of Strategy and Organization. His research focuses on deep tech entrepreneurship.

philipp.schaffer@tum.de
Philipp Lemanczyk

Philipp Lemanczyk

(Alumni)

Philipp Lemanczyk is a doctoral candidate at the Chair of Strategy and Organization. His research focuses on Deep Tech entrepreneurship and AI usage in corporate organizations.

philipp.lemanczyk@tum.de
Our Review Board

Leading Researchers Worldwide

The following scholars from universities around the world serve as members of the TUM Deep Tech Lab Review Board. We are grateful for their guidance and support.

Ann-Kristin Achleitner

Ann-Kristin Achleitner

TUM School of Management

Richard Anson

Richard Anson

University of Bristol

Douglas Arner

Douglas Arner

University of Cambridge

Kurt Beyer

Kurt Beyer

UC Berkeley

Thomas Bohné

Thomas Bohné

University of Cambridge

Malte Brettel

Malte Brettel

RWTH Aachen

Rafael Calvo

Rafael Calvo

Imperial College London

Emilio Castilla

Emilio Castilla

MIT Sloan

Chris Coleridge

Chris Coleridge

University of Cambridge

Paola Criscuolo

Paola Criscuolo

Imperial College London

Pelin Demirel

Pelin Demirel

Imperial College London

Chuck Eesley

Chuck Eesley

Stanford University

Thomas Eisenmann

Thomas Eisenmann

Harvard Business School

Carl Benedikt Frey

Carl Benedikt Frey

University of Oxford

Philipp Gerbert

Philipp Gerbert

TUM Venture Labs

Matthew Grimes

Matthew Grimes

University of Cambridge

Marc Gruber

Marc Gruber

EPFL

Thomas Hellmann

Thomas Hellmann

University of Oxford

Simcha Jong

Simcha Jong

UCL

Stan Karanasios

Stan Karanasios

University of Queensland

Josh Lerner

Josh Lerner

Harvard Business School

Marcela Miozzo

Marcela Miozzo

King's College London

Ramana Nanda

Ramana Nanda

Imperial College London

Ravi Pamnani

Ravi Pamnani

Stanford Biodesign

Lionel Paolella

Lionel Paolella

University of Cambridge

Alex Pentland

Alex Pentland

MIT Media Lab

Jaideep Prabhu

Jaideep Prabhu

University of Cambridge

Raghavendra Rau

Raghavendra Rau

University of Cambridge

Dorottya Sallai

Dorottya Sallai

LSE

Martin Schilling

Martin Schilling

Deep Tech Momentum

Paul Schmiedmayer

Paul Schmiedmayer

Stanford Biodesign

Helmut Schönenberger

Helmut Schönenberger

UnternehmerTUM

Jason Shaw

Jason Shaw

NTU Singapore

Olav Sorenson

Olav Sorenson

UCLA Anderson

Florenta Teodoridis

Florenta Teodoridis

USC Marshall

Frank Tietze

Frank Tietze

University of Cambridge

Stefan Wagner

Stefan Wagner

University of Vienna

Catherine Wang

Catherine Wang

Brunel University London

Affiliations

Partners & Data Sources

Affiliations

Academic Partners

Academic partners
Data Sources

Where Our Data Comes From

Data sources