Naja Kastrup Friis

Naja Kastrup Friis serves as Assistant Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in building physics for Arctic environments. Her research addresses critical challenges in thermal performance, moisture control, and sustainable construction practices for extreme cold climates.

Her educational background includes:

• PhD in Building Engineering, Technical University of Denmark (2023)

Dr. Friis' research program integrates field measurements from Greenlandic test facilities with advanced simulations to optimize building envelopes. She investigates hygrothermal behavior of wall constructions, thermal insulation thickness optimization for GHG reduction, mould growth prevention mechanisms, and life cycle assessment of acoustic materials. Her work directly contributes to UN Sustainable Development Goals through energy-efficient building solutions.

Analysis of her 2023-2025 publications reveals consistent focus on Arctic building challenges, combining experimental field studies in Nuuk and Sisimiut with computational modeling. Key trends include quantification of heat losses in cold climates, development of optimal insulation strategies balancing embodied and operational emissions, and innovative approaches to prevent moisture-related failures in ventilated facades.

Dr. Friis actively participates in major research initiatives including Hampesans: Hempsense (2024-2025), SEEDS electrified heating systems project (2024-2027), and the long-term Arctic Building and Construction program (2018-2026). While specific advisees aren't listed, her role as Assistant Professor involves mentoring students in building engineering and sustainable construction methodologies.

She operates within DTU's Energy and Services research group, maintaining strong collaborations with Greenlandic partners and international researchers. Her field measurement facilities in Arctic settlements serve as critical infrastructure for validating building performance models under extreme conditions.