Research And Development In Science

Turning Europe into a quantum industrial powerhouse Europe has been the cradle of quantum mechanics, the revolutionary science born from the genius of Max Planck, Albert Einstein, Niels Bohr, Erwin Schrödinger, and other visionaries who rewrote the rules of physical reality. On 2 July 2025, in the year marking a centenary since the initial development of quantum mechanics, the Commission has adopted an ambitious European Quantum Strategy, integrating Europe's unique scientific heritage with its vibrant quantum ecosystem of startups, SMEs, large industries, research and technology organisations, academia and research institutes. The mission is clear: turn Europe into a quantum industrial powerhouse that transforms breakthrough science into market-ready applications, while maintaining its scientific leadership. We are imagining a Union where medical scans can detect illnesses at the earliest stages, accelerating from weeks of uncertainty to mere seconds of precise diagnosis; where sensors are able to warn about volcanic activity or water shortages before they happen; and where unprecedented computational power will be available to solve complex problems in logistics, finance and climate modelling. A safer Europe, where our personal data, critical infrastructure, and businesses will always remain private and well-protected; where transport systems are optimised to reduce congestion and prevent accidents; and air travel is guided by quantum-enhanced precision navigation, pinpointing objects' locations down to the centimetre. A greener Europe, where sustainable energy grids can flawlessly manage millions of electric vehicles charging simultaneously overnight. These tangible, transformative technologies are within reach through support from the EU Quantum Strategy. The quantum community has clearly outlined what's needed to achieve this future: · Combine Europe's scientific excellence to bring quantum breakthroughs rapidly to market · Develop advanced quantum supercomputers like the ones we are supporting under the Quantum Flagship and are acquiring under the EuroHPC Joint Undertaking to operate as accelerators next to our leading network of supercomputers · Deploy secure communication networks such as those under EuroQCI, our secure quantum communication infrastructure that will be spanning the whole EU, composed of a terrestrial segment relying on fibre communications networks linking strategic sites at national and cross-border level, and a space segment based on satellites · Support quantum startups and SMEs, enhancing supply chain resilience, and foster supranational innovation clusters · Integrate quantum advancements into strategic capabilities for security and defence, protecting citizens and infrastructure · Educate Europe's workforce through specialised initiatives like the European Quantum Skills Academy Quantum is not one more technology to add to the list; is a high tide that will deeply transform our society and economy.

Here is an interesting career column in Nature by Gerald Schweiger on what he calls a “point of no returns” in research funding. The core idea is simple and uncomfortable. At some point, the total cost of competing for grants becomes equal to, or even higher than, the money that is actually being awarded. Time spent writing proposals, reviewing them, coordinating consortia, and running administrative processes can collectively exceed the value of the funded research itself. When that happens, the system is no longer inefficient. It is extractive. Using the concept of the Szilard point, the author illustrates this with the EU funding call “GenAI for Africa.” Out of 215 proposals, only two are expected to be funded. Depending on the assumptions, the estimated total cost of preparing and evaluating those applications ranges from about €5.3 million to more than €40 million, for a call with a total budget of €5 million. Even the most conservative estimate suggests that taxpayers and researchers may have spent more on the process than on the science. What makes this especially troubling is not just the waste of money, but the waste of attention, energy, and intellectual focus. Early-career researchers learn very quickly that publishing, networking, and even choosing research questions are often subordinated to one overarching goal: securing the next grant. Science becomes optimized for survival in funding competitions rather than for curiosity, rigor, or societal impact. This is not an argument against selectivity or quality control. Scarce resources always require difficult allocation decisions. But it is an argument against pretending that hyper-competition is automatically fair, efficient, or meritocratic. When success rates drop below one percent, we are no longer selecting the best ideas. We are mostly selecting who can afford to play the game longest. If we want to change this, we need to be willing to rethink funding as a system, not just tweak individual calls. More focused calls, staged application procedures, partial lotteries after quality thresholds, or peer-nomination models are not radical ideas. They are pragmatic attempts to reduce systemic waste and redirect effort back to where it belongs. A concrete first step would be this: funders should be required to publicly report not only success rates and awarded budgets, but also estimated application and evaluation costs. Once we routinely ask whether a call is approaching or crossing the Szilard point, it becomes much harder to justify business as usual. Here is the link: https://lnkd.in/dyDqzBNR #Academia #ResearchFunding #AcademicLife #ResearchSystem

The DoD just dropped its FY26 RDT&E budget—and it’s a $179B North Star for anyone building the future of national defense. Here’s what’s hot (and heavily funded): 🤖 Unmanned Systems & Physical AI – The budget is stacked with programs for launched effects, ground robotics, SUAS, TITAN, and AI-enabled C2. This is the golden hour for anyone working in cyber-physical systems, autonomous platforms, and real-world AI at the tactical edge. 🧠 AI/ML & Autonomy – From soldier lethality to ISR and C3I, embedded AI is showing up everywhere. Physical + digital fusion isn’t hype—it’s a requirement. 🚁 Future Vertical Lift & Next-Gen Combat Vehicles – Army and Navy are doubling down on transformational platforms, from long-range assault aircraft to hybrid-electric tracked systems. ⚔️ Hypersonics, Precision Fires & EW – Rapid, smart kill chains are in. Big money flows to hypersonic weapons, integrated fires, and resilient spectrum ops. 🧬 Biotech & Materials Science – Quietly accelerating: synthetic biology, survivability-enhancing materials, and warfighter performance R&D. Big implications for dual-use founders. 🛰️ Tactical Space & Multi-Domain Sensing – LEO, PNT, ISR nodes—space is tactical now, and the budget reflects it. 💻 Digital Pilots & Agile RDT&E – Software-defined everything. Over $1B in funding for digital pilot programs and agile prototyping. If you’re building fast, the DoD wants in. This isn’t just a spending plan—it’s a mission set for innovators. If you’re in unmanned systems, autonomy, biotech, robotics, or defense software… the signal is clear: let’s go. #DoDBudget #RDTandE #DefenseTech #UnmannedSystems #PhysicalAI #Robotics #Biotech #FutureVerticalLift #Hypersonics #DualUse #AgileRDTandE #ISR #GovTech #NationalSecurity

Europe’s science revolution starts now – here’s how to lead It: For decades, The U.S. has been science’s gravitational center. But tectonic shifts are happening—and Europe has a generational opportunity to rewrite the rules. Events like DEI rollbacks, Zuckerberg’s “male energy” comments, and more, have many asking: Is this the future we want to build? This is our moment to win top talent for Europe. But here’s the problem: Europe produces world-class scientists—then watches them flee to Silicon Valley. Why? 🔻Scientists waste 40% of their time on grant paperwork instead of research. 🔻A senior AI researcher in Paris earns 60% less than their counterpart in San Francisco. 🔻Outdated labs and rigid partnerships push talent toward U.S. giants. The fix? Stop competing. Start differentiating. ✅ Turn cities into Science Sandboxes Make cities like Lisbon, Warsaw, and Leipzig tax-free R&D hubs with visa fast lanes and shared supercomputing labs, and startup-university patent co-ownership. Denmark’s BioInnovation Institute did this and tripled the number of spin-offs in five-years. ✅Match U.S. tech salaries - full stop. Europe can’t afford to lowball its brightest minds. Quantum computing, AI, and clean energy pioneers should earn what they’re worth—whether in Barcelona or Munich. ✅ Cut bureaucracy - treat scientists like startup founders Let scientists control budgets like startup founders. Why does hiring a lab manager need eight signatures? A Nobel-caliber battery breakthrough came from Finland’s “no-strings-attached” funding. Let’s scale that autonomy across Europe. Europe might noch have the scale yet, but it has soul. Let’s give scientists and entrepreneurs a reason to stay, build, and redefine what’s possible.

Imagine the reaction if professors told their students that only about 5% would pass an exam, and that their grade would depend largely on the professor’s mood that day. Students would never accept such a system. Yet this is precisely the kind of unfairness professors themselves face when their EU project proposals are evaluated. A picture says more than a thousand words. The graph accompanying this post shows the evaluation scores we received with SIM2 KU Leuven for various Marie Skłodowska-Curie Doctoral Network (MSCA-DN) applications. The red line marks the funding threshold: above it means success, below it means rejection. Over the years, this threshold has climbed steadily, from around 90% in 2013 to over 96% in 2018. Even more concerning is the randomness of the scores. For resubmitted projects with only minor changes, one proposal jumped from 80% to 97%, while another dropped from 94% to 82%. There is only one possible explanation: a deeply flawed reviewing procedure. This graph illustrates why, for several years, Peter Tom Jones and I struggled to motivate ourselves to submit new MSCA-DN applications for our SOLVOMET R&I Centre, despite our strong track record (EREAN, REDMUD, DEMETER, SOCRATES, SULTAN, NEW-MINE). We have since shifted our focus to Research & Innovation Actions (RIA) and Innovation Actions (IA). Unfortunately, we increasingly feel that these evaluations too are becoming unreliable, more akin to a lottery or casino game than a rigorous assessment. I highly recommend the recent reflection on LinkedIn by Peter Tom Jones, Paul McGuiness, and Kostas Komnitsas, which offers a thoughtful analysis of the structural problems in the EU project evaluation process: LinkedIn post: https://lnkd.in/eJxyCeUt LinkedIn article: https://lnkd.in/eamPU6W3 Food for thought. #ResearchFunding #EUProjects #HorizonEurope #SciencePolicy #Horizoneurope #MSCA

How do materials fail, and how can we design stronger, tougher, and more resilient ones? Published in #PNAS, our physics-aware AI model integrates advanced reasoning, rational thinking, and strategic planning capabilities models with the ability to write and execute code, perform atomistic simulations to solicit new physics data from “first principles”, and conduct visual analysis of graphed results and molecular mechanisms. By employing a multiagent strategy, these capabilities are combined into an intelligent system designed to solve complex scientific analysis and design tasks, as applied here to alloy design and discovery. This is significant because our model overcomes the limitations of traditional data-driven approaches by integrating diverse AI capabilities—reasoning, simulations, and multimodal analysis—into a collaborative system, enabling autonomous, adaptive, and efficient solutions to complex, multiobjective materials design problems that were previously slow, expert-dependent, and domain-specific. Wonderful work by my postdoc Alireza Ghafarollahi! Background: The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Our model overcomes these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of LLMs and the dynamic collaboration among AI agents with expertise in various domains, incl. knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities. The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. We demonstrate accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of alloys. Paper: https://lnkd.in/enusweMf Code: https://lnkd.in/eWv2eKwS MIT Schwarzman College of Computing MIT Civil and Environmental Engineering MIT Department of Mechanical Engineering (MechE) MIT Industrial Liaison Program MIT School of Engineering

The European Quantum Industry Consortium (QuIC) has released its official recommendations for the EU Quantum Strategy, outlining key priorities to strengthen Europe’s position in quantum technology and ensure long-term technological leadership, economic growth, and strategic autonomy.   Key Focus Areas in the Recommendations: 👉 Developing a 'Made in Europe' Full-Stack Quantum Computer 👉 Strengthening Europe’s quantum supply chain and reducing dependency on non-EU suppliers 👉 Supporting quantum chip innovation and industrial-scale fabrication 👉 Ensuring secure quantum communications & cryptography 👉 Enhancing funding for quantum startups & scale-ups 👉 Strengthening Europe’s quantum workforce & talent pipeline 👉 Establishing leadership in global quantum technology standards & IP   QuIC underlines its committment to working with EU institutions and industry stakeholders to shape a bold, forward-looking quantum strategy that drives European innovation and competitiveness.   https://lnkd.in/dcKhnnvH #quantum #quantumtechnologies #quantumcomputing #quantumcomminications #quantumsensing #EU

Pretty incredible! A 3000x speed up in our ability to do systematic reviews: "We developed otto-SR, an end-to-end agentic workflow using large language models (LLMs) to support and automate the SR workflow from initial search to analysis. Using otto-SR, we reproduced and updated an entire issue of Cochrane reviews (n=12) in two days, representing approximately 12 work-years of traditional systematic review work. Across Cochrane reviews, otto-SR incorrectly excluded a median of 0 studies (IQR 0 to 0.25), and found a median of 2.0 (IQR 1 to 6.5) eligible studies likely missed by the original authors. Meta-analyses revealed that otto-SR generated newly statistically significant conclusions in 2 reviews and negated significance in 1 review. We found that otto-SR outperformed traditional dual human workflows in SR screening (otto-SR: 96.7% sensitivity, 97.9% specificity; human: 81.7% sensitivity, 98.1% specificity) and data extraction (otto-SR: 93.1% accuracy; human: 79.7% accuracy). These findings demonstrate that LLMs can autonomously conduct and update systematic reviews with superhuman performance, laying the foundation for automated, scalable, and reliable evidence synthesis" Read/download: https://lnkd.in/eupjBMEU

This is the first fully AI-generated paper that has passed the same peer-review process that human scientists go through. The researchers at Sakana AI submitted three papers fully generated by AI (with no human modifications) to an ICLR workshop with the organizers' knowledge and cooperation. Reviewers knew some papers might be AI-generated but weren't told which ones, ensuring an unbiased double-blind review process. One of the three AI-generated papers received an average score of 6.33 from reviewers, placing it above the acceptance threshold and higher than many human-written papers. The accepted paper discussed a negative result related to neural network regularization, showing the AI system could identify and document unsuccessful approaches (an important aspect of scientific research). The papers were withdrawn after review as part of the pre-established protocol, as the scientific community has not yet established norms for publishing fully AI-generated research. What this research demonstrates is that AI systems can now conduct independent scientific inquiry that meets professional standards, which may significantly change how research is conducted and evaluated.

A nice review article "Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation" covers the scope of tools and approaches for how AI can support science. Some of areas the paper covers: (link in comments) 🔎 Literature search and summarization. Traditional academic search engines rely on keyword-based retrieval, but AI-powered tools such as Elicit and SciSpace enhance search efficiency with semantic analysis, summarization, and citation graph-based recommendations. These tools help researchers sift through vast scientific literature quickly and extract key insights, reducing the time required to identify relevant studies. 💡 Hypothesis generation and idea formation. AI models are being used to analyze scientific literature, extract key themes, and generate novel research hypotheses. Some approaches integrate structured knowledge graphs to ground hypotheses in existing scientific knowledge, reducing the risk of hallucinations. AI-generated hypotheses are evaluated for novelty, relevance, significance, and verifiability, with mixed results depending on domain expertise. 🧪 Scientific experimentation. AI systems are increasingly used to design experiments, execute simulations, and analyze results. Multi-agent frameworks, tree search algorithms, and iterative refinement methods help automate complex workflows. Some AI tools assist in hyperparameter tuning, experiment planning, and even code execution, accelerating the research process. 📊 Data analysis and hypothesis validation. AI-driven tools process vast datasets, identify patterns, and validate hypotheses across disciplines. Benchmarks like SciMON (NLP), TOMATO-Chem (chemistry), and LLM4BioHypoGen (medicine) provide structured datasets for AI-assisted discovery. However, issues like data biases, incomplete records, and privacy concerns remain key challenges. ✍️ Scientific content generation. LLMs help draft papers, generate abstracts, suggest citations, and create scientific figures. Tools like AutomaTikZ convert equations into LaTeX, while AI writing assistants improve clarity. Despite these benefits, risks of AI-generated misinformation, plagiarism, and loss of human creativity raise ethical concerns. 📝 Peer review process. Automated review tools analyze papers, flag inconsistencies, and verify claims. AI-based meta-review generators assist in assessing manuscript quality, potentially reducing bias and improving efficiency. However, AI struggles with nuanced judgment and may reinforce biases in training data. ⚖️ Ethical concerns. AI-assisted scientific workflows pose risks, such as bias in hypothesis generation, lack of transparency in automated experiments, and potential reinforcement of dominant research paradigms while neglecting novel ideas. There are also concerns about the overreliance on AI for critical scientific tasks, potentially compromising research integrity and human oversight.